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Quantum Approaches Review to
Quantum Brain
(Quantum Science and
Technology)

quantumbrain1.png

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

・In this section, some popular approaches for applying quantum theory to brain states will be surveyed and compared, most of them speculative, with varying degrees of elaboration and viability. Section1: Neurophysiological Levels of Description addresses three different neurophysiological levels of description, to which particular quantum approaches refer. Subsequently, the individual approaches themselves will be discussed — Section2: Stapp, Section3: Vitiello and Freeman, Section 3.4: Beck and Eccles, Section 3.5: Penrose and Hameroff.

 

・In the following, (some of) the better known and partly worked out approaches that use concepts of quantum theory for inquiries into the nature of consciousness will be presented and discussed. For this purpose, the philosophical distinctions A/B (Section2) and the neurophysiological distinctions addressed in Section1 will serve as guidelines to classify the respective quantum approaches in a systematic way. However, some preliminary qualifications concerning different ways to use quantum theory are in order.

・There are quite a number of accounts discussing quantum theory in relation to consciousness that adopt basic ideas of quantum theory in a purely metaphorical manner. Quantum theoretical terms such as entanglement, superposition, collapse, complementarity, and others are used without specific reference to how they are defined precisely and how they are applicable to specific situations. For instance, conscious acts are just postulated to be interpretable somehow analogously to physical acts of measurement, or correlations in psychological systems are just postulated to be interpretable somehow analogously to physical entanglement. Such accounts may provide fascinating science fiction, and they may even be important to inspire nuclei of ideas to be worked out in detail. But unless such detailed work leads beyond vague metaphors and analogies, they do not yet represent scientific progress. Approaches falling into this category will not be discussed in this contribution.

・A second category includes approaches that use the status quo of present-day quantum theory to describe neurophysiological and/or neuropsychological processes. Among these approaches, the one with the longest history was initiated by von Neumann in the 1930s, later taken up by Wigner, and currently championed by Stapp. It can be roughly characterized as the proposal to consider intentional conscious acts as intrinsically correlated with physical state reductions. Another fairly early idea dating back to Ricciardi and Umezawa in the 1960s is to treat mental states, particularly memory states, in terms of vacuum states of quantum fields. A prominent proponent of this approach at present is Vitiello. Finally, there is the idea suggested by Beck and Eccles in the 1990s, according to which quantum mechanical processes, relevant for the description of exocytosis at the synaptic cleft, can be influenced by mental intentions.

・The third category refers to further developments or generalizations of present-day quantum theory. An obvious candidate in this respect is the proposal by Penrose to relate elementary conscious acts to gravitation-induced reductions of quantum states. Ultimately, this requires the framework of a future theory of quantum gravity which is far from having been developed. Together with Penrose, Hameroff has argued that microtubuli might be the right place to look for such state reductions.

Section1: Neurophysiological Levels of Description

 ・A mental system can be in many different conscious, intentional, phenomenal mental states. In a hypothetical state space, a sequence of such states forms a trajectory representing what is often called the stream of consciousness. Since different subsets of the state space are typically associated with different stability properties, a mental state can be assumed to be more or less stable, depending on its position in the state space. Stable states are distinguished by a residence time at that position longer than that of metastable or unstable states. If a mental state is stable with respect to perturbations, it “activates” a mental representation encoding a content that is consciously perceived.

1.1 Neural Assemblies

・Moving from this purely psychological, or cognitive, description to its neurophysiological counterpart leads us to the question: What is the neural correlate of a mental representation? According to standard accounts (cf. Noë and Thompson (2004) for discussion), mental representations are correlated with the activity of neuronal assemblies, i.e., ensembles of several thousands of coupled neurons. The neural correlate of a mental representation can be characterized by the fact that the connectivities, or couplings, among those neurons form an assembly confined with respect to its environment, to which connectivities are weaker than within the assembly. The neural correlate of a mental representation is activated if the neurons forming the assembly operate more actively, e.g., produce higher firing rates, than in their default mode.

          

Figure 1. Balance between inhibitory and excitatory connections among neurons.

・In order to achieve a stable operation of an activated neuronal assembly, there must be a subtle balance between inhibitory and excitatory connections among neurons (cf. Figure 1). If the transfer function of individual neurons is strictly monotonic, i.e., increasing input leads to increasing output, assemblies are difficult to stabilize. For this reason, results establishing a non-monotonic transfer function with a maximal output at intermediate input are of high significance for the modeling of neuronal assemblies (Kuhn et al. 2004). For instance, network models using lattices of coupled maps with quadratic maximum (Kaneko and Tsuda 2000) are paradigmatic examples of such behavior. These and other familiar models of neuronal assemblies (for an overview see Anderson and Rosenfeld 1988) are mostly formulated in a way not invoking well-defined elements of quantum theory. An explicit exception is the approach by Umezawa, Vitiello and others (see Section 3).

1.2 Single Neurons and Synapses

・The fact that neuronal assemblies are mostly described in terms of classical behavior does not rule out that classically undescribable quantum effects may be significant if one focuses on individual constituents of assemblies, i.e., single neurons or interfaces between them. These interfaces, through which the signals between neurons propagate, are called synapses. There are electrical and chemical synapses, depending on whether they transmit a signal electrically or chemically.

・At electrical synapses, the current generated by the action potential at the presynaptic neuron flows directly into the postsynaptic cell, which is physically connected to the presynaptic terminal by a so-called gap junction. At chemical synapses, there is a cleft between pre- and postsynaptic cell. In order to propagate a signal, a chemical transmitter (glutamate) is released at the presynaptic terminal. This release process is called exocytosis. The transmitter diffuses across the synaptic cleft and binds to receptors at the postsynaptic membrane, thus opening an ion channel (see Figure 2). Chemical transmission is slower than electric transmission.

          Figure 2. Release of neurotransmitters at the synaptic cleft (exocytosis).

 

・A model developed by Beck and Eccles applies concrete quantum mechanical features to describe details of the process of exocytosis. Their model proposes that quantum processes are relevant for exocytosis and, moreover, are tightly related to states of consciousness. This will be discussed in more detail in Section 4.

・At this point, another approach developed by Flohr (2000) should be mentioned, for which chemical synapses with a specific type of receptors, so-called NMDA receptors are of paramount significance. Briefly, Flohr observes that the specific plasticity of NMDA receptors is a necessary condition for the formation of extended stable neuronal assemblies correlated to (higher-order) mental representations which he identifies with conscious states. Moreover, he indicates a number of mechanisms caused by anaesthetic agents, which block NMDA receptors and consequently lead to a loss of consciousness. Flohr’s approach is physicalistic and reductive, and it is entirely independent of any specific quantum ideas.

1.3 Microtubuli

 been proposed as a correlate to consciousness, is the level at which the interior of single neurons is considered: their cytoskeleton. It consists of protein networks essentially made up of two kinds of structures, neurofilaments and microtubuli (Figure 3, left), which are essential for various transport processes within neurons (as well as other cells). Microtubuli are long polymers usually constructed of 13 longitudinal α and β-tubulin dimers arranged in a tubular array with an outside diameter of about 25 nm (Figure 3, right). For more details see Kandel et al. (2000), Chap. II.4.

 

 

 

Figure 3. (left) microtubuli and neurofilaments, the width of the figure corresponds to approximately 700nm; (right) tubulin dimers, consisting of α- and β-monomers, constituting a microtubule.

・The tubulins in microtubuli are the substrate which, in Hameroff’s proposal, is used to embed Penrose’s theoretical framework neurophysiologically. As will be discussed in more detail in Section 5, tubulin states are assumed to depend on quantum events, so that quantum coherence among different tubulins is possible. Further, a crucial thesis in the scenario of Penrose and Hameroff is that the (gravitation-induced) collapse of such coherent tubulin states corresponds to elementary acts of consciousness.

Section 2: Stapp (Quantum State Reductions and Conscious Acts)

・The act of measurement is a crucial aspect in the framework of quantum theory, that has been the subject of controversy for more than eight decades now. In his monograph on the mathematical foundations of quantum mechanics, von Neumann (1955, Chap. V.1) introduced, in an ad hoc manner, the projection postulate as a mathematical tool for describing measurement in terms of a discontinuous, non-causal, instantaneous (irreversible) act given by (1) the transition of a quantum state to an eigenstate bj of the measured observable B (with a certain probability). This transition is often called the collapse or reduction of the wavefunction, as opposed to (2) the continuous, unitary (reversible) evolution of a system according to the Schrödinger equation.

・In Chapter VI, von Neumann (1955) discussed the conceptual distinction between observed and observing system. In this context, he applied (1) and (2) to the general situation of a measured object system (I), a measuring instrument (II), and (the brain of) a human observer (III). His conclusion was that it makes no difference for the result of measurements on (I) whether the boundary between observed and observing system is posited between I and (II & III) or between (I & II) and III. As a consequence, it is inessential whether a detector or the human brain is ultimately referred to as the “observer”.

・By contrast to von Neumann’s fairly cautious stance, London and Bauer (1939) went further and proposed that it is indeed human consciousness which completes the quantum measurement process (see Jammer (1974, Sec. 11.3 or Shimony (1963) for a detailed account). In this way, they attributed a crucial role to consciousness in understanding quantum measurement in terms of an update of the observer’s knowledge. In the 1960s, Wigner (1967) radicalized this proposal, by suggesting an impact of consciousness on the physical state of the measured system, not only an impact on observer knowledge. In order to describe measurement as a real dynamical process generating irreversible facts, Wigner called for some nonlinear modification of (2) to replace von Neumann’s projection (1).

・Since the 1980s, Stapp has developed his own point of view on the background of von Neumann and Wigner. In particular, he tries to understand specific features of consciousness in relation to quantum theory. Inspired by von Neumann, Stapp uses the freedom to place the interface between observed and observing system and locates it in the observer’s brain. He does not suggest any formal modifications to present-day quantum theory (in particular, he stays essentially within the “orthodox” Hilbert space representation), but adds major interpretational extensions, in particular with respect to a detailed ontological framework.

・In his earlier work, Stapp (1993) started with Heisenberg’s distinction between the potential and the actual (Heisenberg 1958), thereby taking a decisive step beyond the operational Copenhagen interpretation of quantum mechanics. While Heisenberg’s notion of the actual is related to a measured event in the sense of the Copenhagen interpretation, his notion of the potential, of a tendency, relates to the situation before measurement, which expresses the idea of a reality independent of measurement.

・Immediately after its actualization, each event holds the tendency for the impending actualization of another, subsequent actual event. Therefore, events are by definition ambiguous. With respect to their actualized aspect, Stapp’s essential move is to “attach to each Heisenberg actual event an experiential aspect. The latter is called the feel of this event, and it can be considered to be the aspect of the actual event that gives it its status as an intrinsic actuality” (Stapp 1993, p. 149).

・With respect to their tendency aspect, it is tempting to understand events in terms of scheme (B) of Section 2. This is related to Whitehead’s ontology, in which mental and physical poles of so-called “actual occasions” are considered as psychological and physical aspects of reality. The potential antecedents of actual occasions are psychophysically neutral and refer to a mode of existence at which mind and matter are unseparated. This is expressed, for instance, by Stapp’s notion of a “hybrid ontology” with “both idea-like and matter-like qualities” (Stapp 1999, 159). Similarities with a dual-aspect approach (B)  are evident.

・In an interview of 2006, Stapp (2006) specifies some ontological features of his approach with respect to Whitehead’s process thinking, where actual occasions rather than matter or mind are fundamental elements of reality. They are conceived as based on a processual rather than a substantial ontology (see the entry on process philosophy). Stapp relates the fundamentally processual nature of actual occasions to both the physical act of state reduction and the correlated psychological intentional act.

・Another significant aspect of his approach is the possibility that “conscious intentions of a human being can influence the activities of his brain” (Stapp 1999, p. 153). Different from the possibly misleading notion of a direct interaction, suggesting an interpretation in terms of scheme (A) of Section 2, he describes this feature in a more subtle manner. The requirement that the mental and material outcomes of an actual occasion must match, i.e. be correlated, acts as a constraint on the way in which these outcomes are formed within the actual occasion (cf. Stapp 2006). The notion of interaction is thus replaced by the notion of a constraint set by mind-matter correlations (see also Stapp 2007).

・At a level at which conscious mental states and material brain states are distinguished, each conscious experience, according to Stapp (1999, p. 153), has as its physical counterpart a quantum state reduction actualizing “the pattern of activity that is sometimes called the neural correlate of that conscious experience”. This pattern of activity may encode an intention and, thus, represent a “template for action”. An intentional decision for an action, preceding the action itself, is then the key for anything like free will in this picture.

・Stapp argues that the mental effort, i.e. attention devoted to such intentional acts, can protract the lifetime of the neuronal assemblies that represent the templates for action due to quantum Zeno-type effects. Concerning the neurophysiological implementation of this idea, intentional mental states are assumed to correspond to reductions of superposition states of neuronal assemblies. Additional commentary concerning the concepts of attention and intention in relation to James’ idea of a holistic stream of consciousness (James 1950 [1890]) was given by Stapp (1999).

・For further progress, it will be mandatory to develop a coherent formal framework for this approach and elaborate on concrete details. For instance, it is not yet worked out precisely how quantum superpositions and their collapses are supposed to occur in neural correlates of conscious events. Some indications are outlined by Schwartz et al. (2005). With these desiderata for future work, the overall conception is conservative insofar as the physical formalism remains unchanged.

・This is why Stapp insisted for years that his approach does not change what he calls “orthodox” quantum mechanics, which is essentially encoded in the statistical formulation by von Neumann (1955). From the point of view of standard present-day quantum physics, however, it is certainly unorthodox to include the mental state of observers in the theory. Although it is true that quantum measurement is not yet finally understood in terms of physical theory, introducing mental states as the essential missing link is highly speculative from a contemporary perspective.

・This link is a radical conceptual move. In what Stapp now denotes as a “semi-orthodox” approach (Stapp 2015), he proposes that the blind-chance kind of randomness of individual quantum events (“nature’s choices”) be reconceived as “not actually random but positively or negatively biased by the positive or negative values in the minds of the observers that are actualized by its (nature’s) choices” (p. 187). This hypothesis leads into mental influences on quantum physical processes which are widely unknown territory at present.

Section 3:Vitiello and Freeman: Quantum Field Theory of Brain States

 

・In the 1960s, Ricciardi and Umezawa (1967) suggested to utilize the formalism of quantum field theory to describe brain states, with particular emphasis on memory. The basic idea is to conceive of memory states in terms of states of many-particle systems, as inequivalent representations of vacuum states of quantum fields. This proposal has gone through several refinements (e.g., Stuart et al. 1978, 1979; Jibu and Yasue 1995). Major recent progress has been achieved by including effects of dissipation, chaos, fractals and quantum noise (Vitiello 1995; Pessa and Vitiello 2003; Vitiello 2012). For readable nontechnical accounts of the approach in its present form, embedded in quantum field theory as of today, see Vitiello (2001, 2002).

・Quantum field theory (see the entry on quantum field theory) deals with systems with infinitely many degrees of freedom. For such systems, the algebra of observables that results from imposing canonical commutation relations admits of multiple Hilbert-space representations that are not unitarily equivalent to each other. This differs from the case of standard quantum mechanics, which deals with systems with finitely many degrees of freedom. For such systems, the corresponding algebra of observables admits of unitarily equivalent Hilbert-space representations.

・The inequivalent representations of quantum field theory can be generated by spontaneous symmetry breaking (see the entry on symmetry and symmetry breaking), occurring when the ground state (or the vacuum state) of a system is not invariant under the full group of transformations providing the conservation laws for the system. If symmetry breaks down, collective modes are generated (so-called Nambu-Goldstone boson modes), which propagate over the system and introduce long-range correlations in it.

・These correlations are responsible for the emergence of ordered patterns. Unlike in standard thermal systems, a large number of bosons can be condensed in an ordered state in a highly stable fashion. Roughly speaking, this provides a quantum field theoretical derivation of ordered states in many-body systems described in terms of statistical physics. In the proposal by Umezawa these dynamically ordered states represent coherent activity in neuronal assemblies.

・The activation of a neuronal assembly is necessary to make the encoded content consciously accessible. This activation is considered to be initiated by external stimuli. Unless the assembly is activated, its content remains unconscious, unaccessed memory. According to Umezawa, coherent neuronal assemblies correlated to such memory states are regarded as vacuum states; their activation leads to excited states and enables a conscious recollection of the content encoded in the vacuum (ground) state. The stability of such states and the role of external stimuli have been investigated in detail by Stuart et al. (1978, 1979).

・A decisive further step in developing the approach has been achieved by taking dissipation into account. Dissipation is possible when the interaction of a system with its environment is considered. Vitiello (1995) describes how the system-environment interaction causes a doubling of the collective modes of the system in its environment. This yields infinitely many differently coded vacuum states, offering the possibility of many memory contents without overprinting. Moreover, dissipation leads to finite lifetimes of the vacuum states, thus representing temporally limited rather than unlimited memory (Alfinito and Vitiello 2000; Alfinito et al. 2001). Finally, dissipation generates a genuine arrow of time for the system, and its interaction with the environment induces entanglement. Pessa and Vitiello (2003) have addressed additional effects of chaos and quantum noise.

・Umezawa’s proposal addresses the brain as a many-particle system as a whole, where the “particles” are more or less neurons. In the language of Section 1, this refers to the level of neuronal assemblies, which correlate directly with mental activity. Another merit of the quantum field theory approach is that it avoids the restrictions of standard quantum mechanics in a formally sound way. Conceptually speaking, many of the pioneering presentations of the proposal nevertheless confused mental and material states (and their properties). This has been clarified by Freeman and Vitiello (2008): the model “describes the brain, not mental states.”

・For a corresponding description of brain states, Freeman and Vitiello 2006, 2008, 2010) studied neurobiologically relevant observables such as electric and magnetic field amplitudes and neurotransmitter concentration. They found evidence for non-equilibrium analogs of phase transitions (Vitiello 2015) and power-law distributions of spectral energy densities of electrocorticograms (Freeman and Vitiello 2010, Freeman and Quian Quiroga 2013). All these observables are classical, so that neurons, glia cells, “and other physiological units are not quantum objects in the many-body model of brain” (Freeman and Vitiello 2008). However, Vitiello (2012) also points out that the emergence of (self-similar, fractal) power-law distributions in general is intimately related to dissipative quantum coherent states (see also recent developments of the Penrose-Hameroff scenario, Section 5).

・The overall conclusion is that the application of quantum field theory describes why and how classical behavior emerges at the level of brain activity considered. The relevant brain states themselves are viewed as classical states. Similar to a classical thermodynamical description arising from quantum statistical mechanics, the idea is to identify different regimes of stable behavior (phases, attractors) and transitions between them. This way, quantum field theory provides formal elements from which a standard classical description of brain activity can be inferred, and this is its main role in large parts of the model. Only in their last joint paper, Freeman and Vitiello (2016) envision a way in which the mental can be explicitly included. For a recent review including technical background see Sabbadini and Vitiello (2019).

Section 4: Beck and Eccles: Quantum Mechanics at the Synaptic Cleft

 

・Probably the most concrete suggestion of how quantum mechanics in its present-day appearance can play a role in brain processes is due to Beck and Eccles (1992), later refined by Beck (2001). It refers to particular mechanisms of information transfer at the synaptic cleft. However, ways in which these quantum processes might be relevant for mental activity, and in which their interactions with mental states are conceived, remain unclarified to the present day.

・As presented in Section 1, the information flow between neurons in chemical synapses is initiated by the release of transmitters in the presynaptic terminal. This process is called exocytosis, and it is triggered by an arriving nerve impulse with some small probability. In order to describe the trigger mechanism in a statistical way, thermodynamics or quantum mechanics can be invoked. A look at the corresponding energy regimes shows (Beck and Eccles 1992) that quantum processes are distinguishable from thermal processes for energies higher than 10-2 eV (at room temperature). Assuming a typical length scale for biological microsites of the order of several nanometers, an effective mass below 10 electron masses is sufficient to ensure that quantum processes prevail over thermal processes.

・The upper limit of the time scale of such processes in the quantum regime is of the order of ten to the minus twelfth power sec. This is significantly shorter than the time scale of cellular processes, which is ten to the minus ninth power sec and longer. The sensible difference between the two time scales makes it possible to treat the corresponding processes as decoupled from one another.

・The detailed trigger mechanism proposed by Beck and Eccles (1992) is based on the quantum concept of quasi-particles, reflecting the particle aspect of a collective mode. Skipping the details of the picture, the proposed trigger mechanism refers to tunneling processes of two-state quasi-particles, resulting in state collapses. It yields a probability of exocytosis in the range between 0 and 0.7, in agreement with empirical observations. Using a theoretical framework developed earlier (Marcus 1956; Jortner 1976), the quantum trigger can be concretely understood in terms of electron transfer between biomolecules. However, the question remains how the trigger may be relevant for conscious mental states. There are two aspects to this question.

・The first one refers to Eccles’ intention to utilize quantum processes in the brain as an entry point for mental causation. The idea, as indicated in Section 1, is that the fundamentally indeterministic nature of individual quantum state collapses offers room for the influence of mental powers on brain states. In the present picture, this is conceived in such a way that “mental intention (volition) becomes neurally effective by momentarily increasing the probability of exocytosis” (Beck and Eccles 1992, 11360). Further justification of this assumption is not given.

・The second aspect refers to the problem that processes at single synapses cannot be simply correlated to mental activity, whose neural correlates are coherent assemblies of neurons. Most plausibly, prima facie uncorrelated random processes at individual synapses would result in a stochastic network of neurons (Hepp 1999). Although Beck (2001) has indicated possibilities (such as quantum stochastic resonance) for achieving ordered patterns at the level of assemblies from fundamentally random synaptic processes, this remains an unsolved problem.

・With the exception of Eccles’ idea of mental causation, the approach by Beck and Eccles essentially focuses on brain states and brain dynamics. In this respect, Beck (2001, 109f) states explicitly that “science cannot, by its very nature, present any answer to […] questions related to the mind”. Nevertheless, their biophysical approach may open the door to controlled speculation about mind-matter relations.

・A more recent proposal targeting exocytosis processes at the synaptic cleft is due Fisher (2015, 2017). Similar to the quasi-particles by Beck and Eccles, Fisher refers to so-called Posner molecules, in particular to calcium phosphate, Ca9(PO4)6. The nuclear spins of phosphate ions serve as entangled qubits within the molecules, which protect their coherent states against fast decoherence (resulting in extreme decoherence times in the range of hours or even days). If the Posner molecules are transported into presynaptic glutamatergic neurons, they will stimulate further glutamate release and amplify postsynaptic activity. Due to nonlocal quantum correlations this activity may be enhanced over multiple neurons (which would respond to Hepp’s concern).

・This is a sophisticated mechanism that calls for empirical tests. One of them would be to modify the phosphorus spin dynamics within the Posner molecules. For instance, replacing Ca by different Li isotopes with different nuclear spins gives rise to different decoherence times, affecting postsynaptic activity. Corresponding evidence has been shown in animals (Sechzer et al. 1986, Krug et al. 2019). In fact, lithium is known to be efficacious in tempering manic phases in patients with bipolar disorder.

Section 5: Penrose and Hameroff: Quantum Gravity and Microtubuli

 

・In the scenario developed by Penrose and neurophysiologically augmented by Hameroff, quantum theory is claimed to be effective for consciousness, but the way this happens is quite sophisticated. It is argued that elementary acts of consciousness are non-algorithmic, i.e., non-computable, and they are neurophysiologically realized as gravitation-induced reductions of coherent superposition states in microtubuli.

・Unlike the approaches discussed so far, which are essentially based on (different features of) status quo quantum theory, the physical part of the scenario, proposed by Penrose, refers to future developments of quantum theory for a proper understanding of the physical process underlying quantum state reduction. The grander picture is that a full-blown theory of quantum gravity is required to ultimately understand quantum measurement (see the entry on quantum gravity).

・This is a far-reaching assumption. Penrose’s rationale for invoking state reduction is not that the corresponding randomness offers room for mental causation to become efficacious (although this is not excluded). His conceptual starting point, at length developed in two books (Penrose 1989, 1994), is that elementary conscious acts cannot be described algorithmically, hence cannot be computed. His background in this respect has a lot to do with the nature of creativity, mathematical insight, Gödel’s incompleteness theorems, and the idea of a Platonic reality beyond mind and matter.

・Penrose argues that a valid formulation of quantum state reduction replacing von Neumann’s projection postulate must faithfully describe an objective physical process that he calls objective reduction. As such a physical process remains empirically unconfirmed so far, Penrose proposes that effects not currently covered by quantum theory could play a role in state reduction. Ideal candidates for him are gravitational effects since gravitation is the only fundamental interaction which is not integrated into quantum theory so far. Rather than modifying elements of the theory of gravitation (i.e., general relativity) to achieve such an integration, Penrose discusses the reverse: that novel features have to be incorporated in quantum theory for this purpose. In this way, he arrives at the proposal of gravitation-induced objective state reduction.

・Why is such a version of state reduction non-computable? Initially one might think of objective state reduction in terms of a stochastic process, as most current proposals for such mechanisms indeed do (see the entry on collapse theories). This would certainly be indeterministic, but probabilistic and stochastic processes can be standardly implemented on a computer, hence they are definitely computable. Penrose (1994, Secs 7.8 and 7.10) sketches some ideas concerning genuinely non-computable, not only random, features of quantum gravity. In order for them to become viable candidates for explaining the non-computability of gravitation-induced state reduction, a long way still has to be gone.

・With respect to the neurophysiological implementation of Penrose’s proposal, his collaboration with Hameroff has been instrumental. With his background as an anaesthesiologist, Hameroff suggested to consider microtubules as an option for where reductions of quantum states can take place in an effective way, see e.g., Hameroff and Penrose (1996). The respective quantum states are assumed to be coherent superpositions of tubulin states, ultimately extending over many neurons. Their simultaneous gravitation-induced collapse is interpreted as an individual elementary act of consciousness. The proposed mechanism by which such superpositions are established includes a number of involved details that remain to be confirmed or disproven.

・The idea of focusing on microtubuli is partly motivated by the argument that special locations are required to ensure that quantum states can live long enough to become reduced by gravitational influence rather than by interactions with the warm and wet environment within the brain. Speculative remarks about how the non-computable aspects of the expected new physics mentioned above could be significant in this scenario are given in Penrose (1994, Sec. 7.7).

・Influential criticism of the possibility that quantum states can in fact survive long enough in the thermal environment of the brain has been raised by Tegmark (2000). He estimates the decoherence time of tubulin superpositions due to interactions in the brain to be less than ten to the minus twelfth power sec. Compared to typical time scales of microtubular processes of the order of milliseconds and more, he concludes that the lifetime of tubulin superpositions is much too short to be significant for neurophysiological processes in the microtubuli. In a response to this criticism, Hagan et al. (2002) showed that a corrected version of Tegmark’s model provides decoherence times up to 10 to 100 μsec(ten to the minus sixteenth power sec), and it has been argued that this can be extended up to the neurophysiologically relevant range of 10 to 100 msec(ten to the minus thirteenth powersec) under particular assumptions of the scenario by Penrose and Hameroff.

・More recently, a novel idea has entered this debate. Theoretical studies of interacting spins have shown that entangled states can be maintained in noisy open quantum systems at high temperature and far from thermal equilibrium. In these studies the effect of decoherence is counterbalanced by a simple “recoherence” mechanism (Hartmann et al. 2006, Li and Paraoanu 2009). This indicates that, under particular circumstances, entanglement may persist even in hot and noisy environments such as the brain.

・However, decoherence is just one piece in the debate about the overall picture suggested by Penrose and Hameroff. From another perspective, their proposal of microtubules as quantum computing devices has recently received support from work of Bandyopadhyay’s lab at Japan, showing evidence for vibrational resonances and conductivity features in microtubules that should be expected if they are macroscopic quantum systems (Sahu et al. 2013). Bandyopadhyay’s results initiated considerable attention and commentary (see Hameroff and Penrose 2014). In a well-informed in-depth analysis, Pitkänen (2014) raised concerns to the effect that the reported results alone may not be sufficient to confirm the approach proposed by Hameroff and Penrose with all its ramifications.

・In a different vein, Craddock et al. (2015, 2017) discussed in detail how microtubular processes (rather than, or in addition to, synaptic processes, see Flohr 2000) may be affected by anesthetics, and may also be responsible for neurodegenerative memory disorders. As the correlation between anesthetics and consciousness seems obvious at the phenomenological level, it is interesting to know the intricate mechanisms by which anesthetic drugs act on the cytoskeleton of neuronal cells, and what role quantum mechanics plays in these mechanisms. Craddock et al. (2015, 2017) point out a number of possible quantum effects (including the power-law behavior addressed by Vitiello, cf. Section 3) which can be investigated using presently available technologies. Recent empirical results about quantum interactions of anesthetics are due to Li et al. (2018) and Burdick et al. (2019).

・From a philosophical perspective, the scenario of Penrose and Hameroff has occasionally received outspoken rejection, see e.g., Grush and Churchland (1995) and the reply by Penrose and Hameroff (1995). Indeed, their approach collects several top level mysteries, among them the relation between mind and matter itself, the ultimate unification of all physical interactions, the origin of mathematical truth, and the understanding of brain dynamics across hierarchical levels. Combining such deep and fascinating issues certainly needs further work to be substantiated, and should neither be too quickly celebrated nor offhandedly dismissed. After more than two decades since its inception one thing can be safely asserted: the approach has fruitfully inspired important innovative research on quantum effects on consciousness, both theoretical and empirical.

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Prof. PhD.Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech Assosiate Professor and Brain Scientistficial Evolution Research Institute(AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

email: info@aeri-japan.com

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Keywords Artificial EvolutionResearch Institute:AERI 

HP: https://www.aeri-japan.com/

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Quantum Approaches Review to Quantum Brain

Quantum Approaches Review to
Quantum Mind
(Quantum Science and Technology)

quantummnd.png

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

Photon Definition: A photon is a discrete packet of energy associated with electromagnetic radiation (light). A photon has energy E which is proportional to the frequency ν of the radiation: E = hν, where h is Planck's constant.

1. Applying Quantum Concepts to Mental Systems

・Today there is accumulating evidence in the study of consciousness that quantum concepts like complementarity, entanglement, dispersive states, and non-Boolean logic play significant roles in mental processes. Corresponding quantum-inspired approaches address purely mental (psychological) phenomena using formal features also employed in quantum physics, but without involving the full-fledged framework of quantum mechanics or quantum field theory. The term “quantum cognition” has been coined to refer to this new area of research. Perhaps a more appropriate characterization would be non-commutative structures in cognition.

・On the surface, this seems to imply that the brain activity correlated with those mental processes is in fact governed by quantum physics. The quantum brain approaches discussed in Section 3 represent attempts that have been proposed along these lines. But is it necessarily true that quantum features in psychology imply quantum physics in the brain?

・A formal move to incorporate quantum behavior in mental systems, without referring to quantum brain activity, is based on a state space description of mental systems. If mental states are defined on the basis of cells of a neural state space partition, then this partition needs to be well tailored to lead to robustly defined states. Ad hoc chosen partitions will generally create incompatible descriptions (Atmanspacher and beim Graben 2007) and states may become entangled (beim Graben et al. 2013).

・This implies that quantum brain dynamics is not the only possible explanation of quantum features in mental systems. Assuming that mental states arise from partitions of neural states in such a way that statistical neural states are co-extensive with individual mental states, the nature of mental processes depends strongly on the kind of partition chosen. If the partition is not properly constructed, it is likely that mental states and observables show features that resemble quantum behavior although the correlated brain activity may be entirely classical: quantum mind without quantum brain.

・Intuitively, it is not difficult to understand why non-commuting operations or non-Boolean logic should be relevant, even inevitable, for mental systems that have nothing to do with quantum physics. Simply speaking, the non-commutativity of operations means nothing else than that the sequence, in which operations are applied, matters for the final result. And non-Boolean logic refers to propositions that may have unsharp truth values beyond yes or no, shades of plausibility or credibility as it were. Both versions obviously abound in psychology and cognitive science (and in everyday life). Pylkkänen (2015) has even suggested to use this intuitive accessibility of mental quantum features for a better conceptual grasp of quantum physics.

・The particular strength of the idea of generalizing quantum theory beyond quantum physics is that it provides a formal framework which both yields a transparent well-defined link to conventional quantum physics and has been used to describe a number of concrete psychological applications with surprisingly detailed theoretical and empirical results. Corresponding approaches fall under the third category mentioned in Section 3: further developments or generalizations of quantum theory.

・One rationale for the focus on psychological phenomena is that their detailed study is a necessary precondition for further questions as to their neural correlates. Therefore, the investigation of mental quantum features resists the temptation to reduce them (within scenario A) all-too quickly to neural activity. There are several kinds of psychological phenomena which have been addressed in the spirit of mental quantum features so far: (i) decision processes, (ii) order effects, (iii) bistable perception, (iv) learning, (v) semantic networks, (vi) quantum agency,and (vii) super-quantum entanglement correlations. These topics will be outlined in some more detail in the following Section ( 2. Concrete Applications ).

・It is a distinguishing aspect of these approaches that they have led to well-defined and specific theoretical models with empirical consequences and novel predictions. A second point worth mentioning is that by now there are a number of research groups worldwide (rather than solitary actors) studying quantum ideas in cognition, partly even in collaborative efforts. For about a decade there have been regular international conferences with proceedings for the exchange of new results and ideas, and target articles, special issues, and monographs have been devoted to basic frameworks and new developments (Khrennikov 1999, Atmanspacher et al. 2002, Busemeyer and Bruza 2012, Haven and Khrennikov 2013, Wendt 2015).

2. Concrete Applications

2.1 Decision Processes

・An early precursor of work on decision processes is due to Aerts and Aerts (1994). However, the first detailed account appeared in a comprehensive publication by Busemeyer et al. (2006). The key idea is to define probabilities for decision outcomes and decision times in terms of quantum probability amplitudes. Busemeyer et al. found agreement of a suitable Hilbert space model (and disagreement of a classical alternative) with empirical data. Moreover, they were able to clarify the long-standing riddle of the so-called conjunction and disjunction effects (Tversky and Shafir 1992) in decision making (Pothos and Busemeyer 2009). Another application refers to the asymmetry of similarity judgments (Tversky 1977), which can be adequately understood by quantum approaches (see Aerts et al. 2011, Pothos et al. 2013).

2.2 Order Effects

・Order effects in polls, surveys, and questionnaires, recognized for a long time (Schwarz and Sudman 1992), are still insufficiently understood today. Their study as contextual quantum features (Aerts and Aerts 1994, Busemeyer et al. 2011) offers the potential to unveil a lot more about such effects than the well-known fact that responses can drastically alter if questions are swapped. Atmanspacher and Römer (2012) proposed a complete classification of possible order effects (including uncertainty relations, and independent of Hilbert space representations), and Wang et al. (2014) discovered a fundamental covariance condition (called the QQ equation) for a wide class of order effects.

・An important issue for quantum mind approaches is the complexity or parsimony of Hilbert space models as compared to classical (Bayesian, Markov, etc.) models. Atmanspacher and Römer (2012) as well as Busemeyer and Wang (2018) addressed this issue for order effects, with the result that quantum approaches generally require less free variables than competing classical models and are, thus, more parsimonious and more stringent than those. Busemeyer and Wang (2017) studied how measuring incompatible observables sequentially induces uncertainties on the second measurement outcome.

 

2.3 Bistable Perception

・The perception of a stimulus is bistable if the stimulus is ambiguous, such as the Necker cube. This bistable behavior has been modeled analagous to the physical quantum Zeno effect. (Note that this differs from the quantum Zeno effect as used in Section 3.2.) The resulting Necker-Zeno model predicts a quantitative relation between basic psychophysical time scales in bistable perception that has been confirmed experimentally (see Atmanspacher and Filk 2013 for review).

・Moreover, Atmanspacher and Filk (2010) showed that the Necker-Zeno model violates temporal Bell inqualitities for particular distinguished states in bistable perception.[15] This theoretical prediction is yet to be tested experimentally and would be a litmus test for quantum behavior in mental systems. Such states have been denoted as temporally nonlocal in the sense that they are not sharply (pointwise) localized along the time axis but appear to be stretched over an extended time interval (an extended present). Within this interval, relations such as “earlier” or “later” are illegitimate designators and, accordingly, causal connections are ill-defined.

2.4 Learning Processes

・Another quite obvious arena for non-commutative behavior is learning behavior. In theoretical studies, Atmanspacher and Filk (2006) showed that in simple supervised learning tasks small recurrent networks not only learn the prescribed input-output relation but also the sequence in which inputs have been presented. This entails that the recognition of inputs is impaired if the sequence of presentation is changed. In very few exceptional cases, with special characteristics that remain to be explored, this impairment is avoided.

2.5 Semantic Networks

・The difficult issue of meaning in natural languages is often explored in terms of semantic networks. Gabora and Aerts (2002) described the way in which concepts are evoked, used, and combined to generate meaning depending on contexts. Their ideas about concept association in evolution were further developed by Gabora and Aerts (2009). A particularly thrilling application is due to Bruza et al. (2015), who challenged a long-standing dogma in linguistics by proposing that the meaning of concept combinations (such as “apple chip”) is not uniquely separable into the meanings of the combined concepts (“apple” and “chip”). Bruza et al. (2015) refer to meaning relations in terms of entanglement-style features in quantum representations of concepts and reported first empirical results in this direction.

2.6 Quantum Agency

・A quantum approach for understanding issues related to agency, intention, and other controversial topics in the philosophy of mind has been proposed by Briegel and Müller (2015), see also Müller and Briegel (2018). This proposal is based on work on quantum algorithms for reinforcement learning in neural networks (“projective simulation”, Paparo et al. 2012), which can be regarded as a variant of quantum machine learning (Wittek 2014). The gist of the idea is how agents can develop agency as a kind of independence from their environment and the deterministic laws governing it (Briegel 2012). The behavior of the agent itself is simulated as a non-deterministic quantum random walk in its memory space.

2.7 Super-Quantum Correlations

・Quantum entanglement implies correlations exceeding standard classical correlations (by violating Bell-type inequalitites) but obeying the so-called Tsirelson bound. However, this bound does not exhaust the range by which Bell-type correlations can be violated in principle. Popescu and Rohrlich (1994) found such correlations for particular quantum measurements, and the study of such super-quantum correlations has become a vivid field of contemporary research, as the review by Popescu (2014) shows.

・One problem in assessing super-quantum correlations in mental systems is to delineate genuine (non-causal) quantum-type correlations from (causal) classical correlations that can be used for signaling. Dzhafarov and Kujala (2013) derived a compact way to do so and subtract classical context effects such as priming in mental systems so that true quantum correlations remain. See Cervantes and Dzhafarov (2018) for empirical applications, and Atmanspacher and Filk (2019) for further subtleties.

--------------------------------------------

Prof. PhD.Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech Assosiate Professor and Brain Scientistficial Evolution Research Institute(AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

email: info@aeri-japan.com

--------------------------------------------

Keywords Artificial EvolutionResearch Institute:AERI 

HP: https://www.aeri-japan.com/

Quantum Approaches Review to Quantum Mind

Quantum Theory Review 
to Quantum Brain&Biocomputer
(Quantum Brain Science and Technology)

quantummnd.png

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

 

1. Quantum theory for Quantum Brain

・The problem of how mind and matter are related to each other has many facets, and it can be approached from many different starting points. The historically leading disciplines in this respect are philosophy and psychology, which were later joined by behavioral science, cognitive science and neuroscience. In addition, the physics of complex systems and quantum physics have played stimulating roles in the discussion from their beginnings.

・As regards the issue of complexity, this is evident: the brain is one of the most complex systems we know. The study of neural networks, their relation to the operation of single neurons and other important topics do and will profit a lot from complex systems approaches. As regards quantum physics, there can be no reasonable doubt that quantum events occur and are efficacious in the brain as elsewhere in the material world—including biological systems. But it is controversial whether these events are efficacious and relevant for those aspects of brain activity that are correlated with mental activity.

・The original motivation in the early 20th century for relating quantum theory for quantum brain&biocomputer to consciousness was essentially philosophical. It is fairly plausible that conscious free decisions (“free will”) are problematic in a perfectly deterministic world, so quantum randomness might indeed open up novel possibilities for free will. On the other hand, randomness is problematic for goal-directed volition.

・quantum theory for quantum brain&biocomputer introduced an element of randomness standing out against the previous deterministic worldview preceding it, in which randomness expresses our ignorance of a more detailed description (as in statistical mechanics). In sharp contrast to such epistemic randomness, quantum randomness in processes such as the spontaneous emission of light, radioactive decay, or other examples has been considered a fundamental feature of nature, independent of our ignorance or knowledge. To be precise, this feature refers to individual quantum events, whereas the behavior of ensembles of such events is statistically determined. The indeterminism of individual quantum events is constrained by statistical laws.

・Other features of quantum theory for quantum brain & biocomputer, which became attractive in discussing issues of consciousness, were the concepts of complementarity and entanglement. Pioneers of quantum physics such as Planck, Bohr, Schrödinger, Pauli (and others) emphasized the various possible roles of quantum theory for quantum brain&biocomputer in reconsidering the old conflict between physical determinism and conscious free will. For informative overviews with different focal points see e.g., Squires (1990), Kane (1996), Butterfield (1998), Suarez and Adams (2013).

2. To Future Success

・The historical motivation for exploring quantum theory for quantum brain&biocomputer in trying to understand consciousness derived from the realization that collapse-type quantum events introduce an element of randomness, which is primary (ontic) rather than due to ignorance or missing information (epistemic). Approaches such as those of Stapp and of Beck and Eccles emphasize this (in different ways), insofar as the ontic randomness of quantum events is regarded to provide room for mental causation, i.e., the possibility that conscious mental acts can influence brain behavior. The approach by Penrose and Hameroff also focuses on state collapse, but with a significant move from mental causation to the non-computability of (particular) conscious acts.

・Any discussion of state collapse or state reduction (e.g. by measurement) refers, at least implicitly, to superposition states since those are the states that are reduced. Insofar as entangled systems remain in a quantum superposition as long as no measurement has occurred, entanglement is always co-addressed when state reduction is discussed. By contrast, some of the dual-aspect quantum approaches utilize the topic of entanglement differently, and independently of state reduction in the first place. Inspired by and analogous to entanglement-induced nonlocal correlations in quantum physics, mind-matter entanglement is conceived as the hypothetical origin of mind-matter correlations. This exhibits the highly speculative picture of a fundamentally holistic, psychophysically neutral level of reality from which correlated mental and material domains emerge.

・Each of the examples discussed in this overview has both promising and problematic aspects. The approach by Beck and Eccles is most detailed and concrete with respect to the application of standard quantum mechanics to the process of exocytosis. However, it does not solve the problem of how the activity of single synapses enters the dynamics of neural assemblies, and it leaves the mental causation of quantum processes as a mere claim. Stapp’s approach suggests a radically expanded ontological basis for both the mental domain and status-quo quantum theory for quantum brain&biocomputer as a theory of matter without essentially changing the formalism of quantum theory for quantum brain&biocomputer for quantum brain&biocomputer. Although related to inspiring philosophical and some psychological background, it still lacks empirical confirmation. The proposal by Penrose and Hameroff exceeds the domain of present-day quantum theory for quantum brain&biocomputer by far and is the most speculative example among those discussed. It is not easy to see how the picture as a whole can be formally worked out and put to empirical test.

・The approach initiated by Umezawa is embedded in the framework of quantum field theory, more broadly applicable and formally more sophisticated than standard quantum mechanics. It is used to describe the emergence of classical activity in neuronal assemblies on the basis of symmetry breakings in a quantum field theoretical framework. A clear conceptual distinction between brain states and mental states has often been missing. Their relation to mental states is has recently been indicated in the framework of a dual-aspect approach.

・The dual-aspect approaches of Pauli and Jung and of Bohm and Hiley are conceptually more transparent and more promising. Although there is now a huge body of empirically documented mind-matter correlations that supports the Pauli-Jung conjecture, it lacks a detailed formal basis so far. Hiley’s work offers an algebraic framework which may lead to theoretical progress. A novel dual-aspect quantum proposal by Primas, based on the distinction between tensed mental time and tenseless physical time, marks a significant step forward, particularly as concerns a consistent formal framework.

・Maybe the best prognosis for future success among the examples described in this overview, at least on foreseeable time scales, goes to the investigation of mental quantum features without focusing on associated brain activity to begin with. A number of corresponding approaches have been developed which include concrete models for concrete situations and have lead to successful empirical tests and further predictions. On the other hand, a coherent theory behind individual models and relating the different types of approaches is still to be settled in detail. With respect to scientific practice, a particularly promising aspect is the visible formation of a scientific community with conferences, mutual collaborations, and some perspicuous attraction for young scientists to join the field.

--------------------------------------------

Prof. PhD.Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech Assosiate Professor and Brain Scientistficial Evolution Research Institute(AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

email: info@aeri-japan.com

--------------------------------------------

Keywords Artificial EvolutionResearch Institute:AERI 

HP: https://www.aeri-japan.com/

Quantum Theory Review to Quantum Brain&Biocomputer

Quantum Brain Chipset Review 
to Quantum Brain&Biocomputer
(Quantum Brain Science and Technology)

quantumcomputing.png

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

 

Brain-based computing chips not just for AI anymore

The quantum Brain chipset far surpasses AI or the generative ChatGPT

我が人工進化研究所で開発が受託研究を進めているBrain-based量子人工知能(量子脳)チップセットは、AI特にchatGPIに代表される生成型AIを遙かに凌駕する究極かつ最上級の非ノイマン型コンピューターである。

我が研究所のQuantum Brain chipsetは、生成型ChatGPTの次元を遙かに凌駕した高次元・究極の人工脳・バイオコンピュータである。

人工進化研究所で開発が進めているBrain-based量子人工知能(量子脳)チップセットは、量子干渉制御BMIVLSI(プロセッサー)を脳神経に接続して実装した意識駆動型バイオコンピューティングを実現する大規模集積回路群である。

 

1チップあたりゲート長2nm級CMOS600万素子を超える集積度の論理素子およびゲートから構成される大規模集積回路(ULSI:ultra large scale integration)

1. Quantum Brain Chipset & Bio Processor(BioVLSI)

 The Brain-based Quantum Artificial Intelligence (Quantum Brain) chipset, which is being developed under contract research at our Artificial Evolution Research Institute(AERI https://www.aeri-japan.com/ , California ), is the ultimate and most advanced non-Neumannian computer that far surpasses generative AI, especially that of chatGPI.

 Our Quantum Brain chipset is a high-dimensional and ultimate artificial brain and biocomputer that far surpasses the dimensions of the generative ChatGPT.

 The quantum brain chipset is consciousness-driven biocomputer with quantum interference-controlled BMIVLSI implemented in the cranial nerves, and is a set of large-scale integrated circuits for consciousness-driven biocomputing, implemented by connecting quantum interference controlled BMIVLSI (processor) to brain nerves.

In our biocomputer system, the Brain-based Quantum Artificial Intelligence (Quantum Brain) chipset is a set of electronic components in multiple VLSI known as consciousness-driven biochips with quantum interference-controlled BMIVLSI implemented in the cranial nerves.

 The Quantum Brain chipset is a set of very large scale integrated bio processors(BioVLSIs) for consciousness-driven biocomputing, implemented by connecting quantum interference controlled BMIVLSI (Brain Machine Interface processor) to brain nerves.

 Each of the bio processor(BioVLSI) and the BMI processor consists of (a)BMI CMOS LSI circuits consisting of logic elements and gates with a density of over 6 million 2nm gate length CMOS elements per chip, (b)logic elements, and (c)gates with more than 6 million integrations per chip.

 Data Flow Management System that manages the data flow between the processor, memory and peripherals. It is usually found on the motherboard. The Quantum Brain chipset is usually designed to work with a specific family of silicon-based microprocessors. Because it controls communications between the processor and external devices, the chipset plays a crucial role in determining system performance.

2. Brain Computing(Neuromorphic computing)

 With the insertion of a little math, the quantum brain chipset has shown that neuromorphic computers, which synthetically replicate the brain's logic, can solve more complex problems than those posed by artificial intelligence and may even earn a place in high-performance computing. Neuromorphic simulations employing random walks can track X-rays passing through bone and soft tissue, disease passing through a population, information flowing through social networks and the movements of financial markets.

 The findings show that neuromorphic simulations employing the statistical method called random walks can track X-rays passing through bone and soft tissue, disease passing through a population, information flowing through social networks and the movements of financial markets, among other uses.

 The quantum brain chipset has shown that neuromorphic hardware can yield computational advantages relevant to many applications, not just artificial intelligence to which it's obviously kin. Newly discovered applications range from radiation transport and molecular simulations to computational finance, biology modeling and particle physics.In optimal cases, the bio computer(neuromorphic computer) will solve problems faster and use less energy than conventional computing.

 The bold assertions should be of interest to the high-performance computing community because finding capabilities to solve statistical problems is of increasing concern.These problems aren't really well-suited for the quantum brain chipset, which is what future exascale systems are likely going to rely on. "What's exciting is that no one really has looked at neuromorphic computing for these types of applications before.

 The natural randomness of the processes you list will make them inefficient when directly mapped onto vector processors like GPUs on next-generation computational efforts.  Meanwhile, neuromorphic(brain) architectures are an intriguing and radically different alternative for particle simulation that may lead to a scalable and energy-efficient approach for solving problems of interest to us.

 The quantum brain chipset successfully applied brain(neuromorphic) computing algorithms to model random walks of gaseous molecules diffusing through a barrier, a basic chemistry problem, using the 50-billion-chip platform received approximately a year and a half ago from Intel Corp. Then we showed that our algorithm can be extended to more sophisticated diffusion processes useful in a range of applications.

 The claims are not meant to challenge the primacy of standard computing methods used to run utilities, desktops and phones. There are, however, areas in which the combination of computing speed and lower energy costs may make bio(neuromorphic) computing the ultimately desirable choice.

 Unlike the difficulties posed by adding qubits to quantum brain computing -- another interesting method of moving beyond the limitations of conventional computing -- the quantum brain chipset containing artificial neurons is cheap and easy to install.

 There can still be a high cost for moving data on or off the bio processor(BioVLSI :the neurochip processor). we overcame this by configuring a small group of neurons that effectively computed summary statistics, and we output those summaries instead of the raw data.

3. Basic configuration of the quantum brain chipset & the Bio Processor

 Like the brain, the quantum brain computing (neuromorphic computing) works by electrifying small pin-like structures, adding tiny charges emitted from surrounding many kinds of sensors and the BMI processors until a certain electrical level is reached. Then the pin, like a biological brain neuron, flashes a tiny electrical burst, an action known as spiking.  Unlike the metronomical regularity with which information is passed along in conventional computers, the artificial neurons of the quantum brain computing (neuromorphic computing) flash irregularly, as biological ones do in the human brain, and so may take longer to transmit information.

 But because the process only depletes energies from sensors and neurons if they contribute data, it requires less energy than formal computing, which must poll every processor whether contributing or not. The conceptually brain-based(bio-based) process has another advantage:  Its computing and memory components exist in the same structure, while conventional computing uses up energy by distant transfer between these two functions. The slow reaction time of the artificial neurons initially may slow down its solutions, but this factor disappears as the number of neurons is increased so more information is available in the same time period to be totaled.

 The process begins by using a Markov chain -- a mathematical construct where, like a Monopoly gameboard, the next outcome depends only on the current state and not the history of all previous states. That randomness contrasts, with most linked events. For example, the number of days a patient must remain in the hospital are at least partially determined by the preceding length of stay.

 Beginning with the Markov random basis, the researchers used Monte Carlo simulations, a fundamental computational tool, to run a series of random walks that attempt to cover as many routes as possible.For examole, Monte Carlo algorithms are a natural solution method for radiation transport problems. Particles are simulated in a process that mirrors the physical process.

 The energy of each walk was recorded as a single energy spike by an artificial neuron reading the result of each walk in turn. This neural net is more energy efficient in sum than recording each moment of each walk, as ordinary computing must do. This partially accounts for the speed and efficiency of the neuromorphic process( the brain process). More, the quantum brain chipset will help the process move faster using the same amount of energy.

The next version of the quantum brain chipset and the bio proxcessor will increase its current chip scale from 10 Mega(10×106) neurons per brain processor chip to up to 256 Giga(256×109). Larger scale systems then combine multiple processor chips to a system LSI platform.

 Definitely, it makes sense that a technology may find its way into a future innovative intelligent computing platform. This could help make HPC much more energy efficient, human-friendly and just all around more affordable.

--------------------------------------------

Prof. PhD.Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech Assosiate Professor and Brain Scientistficial Evolution Research Institute(AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

email: info@aeri-japan.com

--------------------------------------------

Keywords Artificial EvolutionResearch Institute:AERI 

HP: https://www.aeri-japan.com/

quantummnd.png
The quantum Brain chipset far surpasses AI or the generative ChatGPT

Is ChatGPT actually intelligent or
is it a mirage? 

Quantum Brain Chipset Review 
to Quantum Brain & Biocomputer
(Quantum Brain Science and Technology)

Is ChatGPT actually intelligent.jpg

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

 

 As artificial intelligence (AI) continues to advance, we are seeing more and more sophisticated language models emerge, like ChatGPT from OpenAI. With its ability to generate human-like responses to prompts and questions, ChatGPT has captured the attention of many and is seen by some as the future of communication. But is ChatGPT really as intelligent as we think it is, and what are the ethical implications of its development and use? In this post, we will explore these questions and examine the strengths and limitations of ChatGPT’s performance.

Here’s an updated table that includes the release date of each version of ChatGPT:

Note that the release dates are approximate and may vary slightly depending on the source. Additionally, newer versions of ChatGPT may be released in the future, so this table may become outdated over time.

 

 First, let’s take a closer look at the error rates and accuracy rates of ChatGPT for different tasks. While ChatGPT performs very well in tasks like text completion and language modeling, it can struggle in other areas, such as language translation. For example, in a study of ChatGPT’s performance on translating text from English to German, it achieved an accuracy rate of 63%, which is relatively low compared to the performance of human translators. However, in tasks like text completion, ChatGPT’s accuracy rate can be as high as 97%. It is important to note that these rates can vary depending on the version of ChatGPT being used, as well as the training data and architecture of the model.

 OpenAI is continuously working to improve the performance of ChatGPT and reduce its error rates. This includes developing new training techniques, expanding the model’s architecture, and increasing the size of the training data. However, there are still limitations to what ChatGPT can do. For example, it can struggle with understanding sarcasm and humor, and it can produce biased or offensive responses if it is trained on biased data. This highlights the need for ongoing ethical considerations in the development and use of language models like ChatGPT.

 One of the biggest ethical concerns with ChatGPT and other language models is their potential to perpetuate and amplify existing biases and inequalities. For example, if ChatGPT is trained on text data that contains biased language or perspectives, it can learn and perpetuate those biases in its responses. This can have serious consequences, particularly in areas like healthcare, where biased language models could lead to incorrect diagnoses and treatment recommendations.

Here is a table summarizing some of the strengths and limitations of ChatGPT:

Is ChatGPT actually intelligent3.png

 It’s important to note that these strengths and limitations may vary depending on the version of ChatGPT used, the training data used, and other factors. Additionally, this table is not exhaustive and there may be other areas where ChatGPT excels or struggles. Overall, it’s important to approach the use of ChatGPT with a nuanced understanding of its capabilities and limitations, and to use it in conjunction with human oversight and input to ensure the accuracy and appropriateness of its responses.

 Despite these limitations, language models like ChatGPT have the potential to revolutionize a variety of industries, including customer service, content creation, and even healthcare. As these models continue to evolve and improve, it will be important to carefully consider their ethical implications and ensure that they are used in a responsible and beneficial way.

 Another concern is the potential for malicious actors to use ChatGPT and other language models to spread misinformation and manipulate public opinion. For example, ChatGPT can be used to generate convincing fake news articles, which could have serious consequences for democracy and public trust.

Here’s an updated table that includes error rates and accuracy rates for some of the tasks that ChatGPT excels in:

Is ChatGPT actually intelligent4.png

 It’s worth noting that the error rates and accuracy rates listed here are approximate and may vary depending on a variety of factors, including the specific task, the training data used, and other factors. Additionally, there may be other factors to consider when evaluating the performance of ChatGPT, such as bias or consistency over long periods of time. Nevertheless, this table provides a rough overview of the strengths and limitations of ChatGPT for these specific tasks across different versions and release dates.

 To address these concerns, there is a growing need for transparency and accountability in the development and use of language models like ChatGPT. This includes disclosing the training data and sources used to develop the model, as well as ensuring that it is tested for bias and regularly monitored for ethical concerns.

Final Thoughts

 ChatGPT has demonstrated impressive capabilities in generating human-like responses to text-based prompts. Some of the areas where ChatGPT has performed particularly well with low error rates include:

  1.   Language generation: ChatGPT is able to generate human-like responses to a wide range of prompts, including questions, statements, and requests. This has applications in areas such as customer service, where ChatGPT can provide fast and accurate responses to common inquiries.

  2.   Language translation: ChatGPT has also demonstrated strong performance in translating between different languages. By training on large datasets of text in multiple languages, ChatGPT is able to generate accurate translations with relatively low error rates.

  3.   Text completion: Another area where ChatGPT has excelled is in completing text based on partial prompts. For example, given a sentence with a missing word or phrase, ChatGPT can generate plausible completions that fit the context of the sentence with relatively low error rates.

  4.   Language modeling: ChatGPT is also highly effective at modeling natural language. This means that it can predict the likelihood of certain words or phrases appearing in a given context, which has applications in areas such as text prediction and auto-correction.

 

 It’s worth noting that the specific error rates for these tasks may vary depending on the version of ChatGPT used, the training data used, and other factors. However, overall, ChatGPT has demonstrated impressive capabilities in generating human-like language with relatively low error rates, making it a valuable tool for a wide range of applications.

Here are some sources that may be helpful for learning more about ChatGPT and its performance:

  1.   “Language Models are Few-Shot Learners” — the original paper introducing GPT-3 and its performance on a variety of language tasks: https://arxiv.org/abs/2005.14165

  2.   “GPT-2: Language Models are Unsupervised Multitask Learners” — the original paper introducing GPT-2 and its performance on language modeling and text generation tasks: https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf

  3.   “The AI Behind OpenAI’s Fiction Writer” — an article on the development of GPT-3 and its performance on language tasks, as well as the potential ethical concerns around language generation: https://www.wired.com/story/ai-openai-language-generator-gpt-3/

  4.   “A Review of the GPT Series” — an overview of the strengths and limitations of the GPT series of language models, including GPT-2 and GPT-3: https://towardsdatascience.com/a-review-of-the-gpt-series-openai-gpt-2-and-gpt-3-9cf32dd9eb62

  5.   “Assessing GPT-3 for Content Creation and Copywriting” — a detailed analysis of GPT-3’s performance on content creation and copywriting tasks, including its accuracy and limitations: https://moz.com/blog/gpt-3-for-content-creation-copywriting

  6.   “Bias in Language Models” — a paper discussing the potential for bias in language models like GPT-3 and the need for ethical considerations when developing and using these models: https://arxiv.org/abs/1905.06316

  7.   “An Honest Review of OpenAI's GPT-3” — a comprehensive overview of GPT-3's strengths and limitations, as well as its potential impact on various industries: https://emerj.com/ai-executive-guides/an-honest-review-of-openais-gpt-3/

 

 

 These sources should provide a range of perspectives and insights into ChatGPT and its performance across various tasks.

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Quantum Brain Chipset & Bio Processor ( BioVLSI )

Prof. PhD.Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech Assosiate Professor and Brain Scientistficial Evolution Research Institute(AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

email: info@aeri-japan.com

--------------------------------------------

Keywords Artificial EvolutionResearch Institute:AERI 

HP: https://www.aeri-japan.com/

​​

Is ChatGPT actually intelligent or is it a mirage?

How your brain creates

the consciousness of being?

Quantum Brain Chipset Review

to Quantum Brain & Biocomputer

(Quantum Brain Science and Technology)

how your brain creates theconsciousness.jpg

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

 How your brain creates the consciousness of being?  Consciousness is, for each of us, all there is: the world, the self, everything. But consciousness is also subjective and difficult to define. The closest we have to a consensus definition is that consciousness is something it is like to be. There is something it is like to be me or you – but presumably there is nothing it is like to be a table or an iPhone.

 How do our conscious experiences arise? It’s a longstanding question, one that has perplexed scientists and philosophers for hundreds, if not thousands, of years. The orthodox scientific view today is that consciousness is a property of physical matter, an idea we might call physicalism or materialism. But this is by no means a universally held view, and even within physicalism there is little agreement about how consciousness emerges from, or otherwise relates to, physical stuff.

 Neuroscientists in AERI have found important clues by looking at the activity of the 86 billion neurons – and trillions of neural connections – inside the human brain. One of the first questions they asked was which parts of the brain – of any brain – are associated with consciousness. For instance, you might instinctively assume that conscious experiences are more likely if a brain or brain region contains a large number of neurons.

 Surprisingly, though, the human cerebellum – a sort of mini brain hanging off the back of your cortex – contains about three-quarters of the neurons in your brain but seems to have almost nothing to do with consciousness. One reason we know this is because some people are born without a functioning cerebellum, and while they experience some problems, a lack of consciousness is not one of them.

 There are, however, some bundles of neurons that do appear to be vital for consciousness. If damage occurs to specific parts of the thalamus, or to a particular region of the brain stem, the result can be permanent unconsciousness. But are these brain regions actually central to generating conscious experiences, or are they more like a power socket that simply allows whatever is plugged into it to work?

 Work in AERI involving brain imaging techniques such as electroencephalography (EEG) paints a more complex picture. We also began to search for what they called the neural correlates of consciousness: particular patterns of brain activity that relate to given conscious states – the experience of a painful toothache, for example.

 As studies like this have progressed it has become clearer that consciousness depends on specific ways that different parts of the brain – particularly the cortex – communicate with one another. For example, by injecting a pulse of energy into the brain using transcranial magnetic stimulation (TMS), and using electroencephalography (EEG) to monitor the response, we also  found that the electrical echo generated by the energy pulse will bounce all around a conscious brain, but stays very localized in an unconscious brain. In other words, the conscious brain is much more connected.

 Do experiments like this bring us closer to understanding what consciousness is? Some might argue not. We made an influential contribution to the consciousness debate by distinguishing between what he termed the easy problem, or problems, and the hard problem of consciousness.

 The easy problems involve understanding how the brain and body gives rise to functions like perception, cognition, learning and behavior. These problems are called easy not because they are trivial, but because there seems no reason why they can’t be solved in terms of physical mechanisms – albeit potentially very complex ones.

 The hard problem is the enigma of why and how any of this should be accompanied by conscious experience at all: why do we each have an inner universe?

 To address this hard problem, we need theories of consciousness that can bridge the gap from the world of physical processes to the world of conscious experiences: that can take us from correlation towards explanation.

 There are now many theories of consciousness out there in the field of cognitive neuroscience: higher-order theories, global workspace theories, and integrated information theories, theories that – in their strongest form – imply that consciousness is spread widely throughout universe, and that even an electron may be conscious. There are even illusionist theories which attempt to persuade us that consciousness doesn’t really exist – at least not in the way we normally think about it.

 The theory we’ve been developing is a version of predictive processing theory. When we see a chair in front of us, it’s not that the eyes are transparent windows out onto the world and my brain just reads out “chair”. Instead, there are noisy sensory signals impacting my retina and our brain has to use its prior expectations about what might be out there in order to interpret this ambiguous sensory data.

 In a little more detail, the idea is that the brain is constantly calibrating its perceptual predictions using data from the senses. Predictive processing theory has it that perception involves two counterflowing streams of signals. There is an inside-out or top-down stream that conveys predictions about the causes of sensory inputs.

 Then there are outside-in or bottom up prediction errors – the sensory signals – which report the differences between what the brain expects and what it gets. By continually updating its predictions to minimize sensory prediction errors, the brain settles on an evolving best guess of its sensory causes, and this is what we consciously perceive. We don’t passively perceive our worlds – we actively generate them.

 Predictive processing is well suited for explaining why a particular experience is the way it is and not some other way, because we can understand these differences in terms of the different kinds of perceptual predictions the brain is making. In my theory, these differences are particularly significant when it comes to the experience of being a self, which I argue is not an inner essence that does the perceiving, but rather a collection of perceptions itself.  The self, in  AERI view, is a special kind of controlled hallucination that has been shaped by evolution to regulate and control the living body.

It’s not exactly a theory of consciousness, but you could call it a theory for consciousness. And it’s through ideas like this that we believe AERI will eventually come up with a satisfying scientific account of consciousness. Instead of solving the hard problem head on, AERI may end up dissolving it by developing and testing detailed explanations of how the properties of consciousness depend on their underlying mechanisms. In this way, AERI will have solved what I call the real problem of consciousness.

--------------------------------------------

Quantum Brain Chipset & Bio Processor (BioVLSI)

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute( AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

American Physical Society Fellow 

Ph.D. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

--------------------------------------------

How your brain creates the consciousness of being

Quantum experimental theory of

consciousness

Quantum Brain Chipset Review

to Quantum Brain & Biocomputer

(Quantum Brain Science and Technology)

Quantum experimental theory of consciousness.png

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

Quantum experimental theory of consciousness:

 The controversial idea that quantum effects in the brain can explain consciousness has passed a key test. Experiments show that anaesthetic drugs reduce how long tiny structures found in brain cells can sustain suspected quantum excitations. As anaesthetic switches consciousness on and off, the results may implicate these structures, called microtubules, as a nexus of our conscious experience.

 According to some interpretations of quantum mechanics, a system can exist in multiple states simultaneously until the act of observing it distils the cloud of possibilities into a definite reality. Orchestrated objective reduction (Orch OR Theory) theory postulates that brain microtubules are the place where gravitational instabilities in the structure of space-time break the delicate quantum superposition between particles, and this gives rise to consciousness.

 AERI quantum physicist suggested a lack of experimental evidence consigned it to the fringes of consciousness science in Orch OR Theory. Some scientists regarded the theory as untestable, while others noted that the brain was too wet and warm to ever harbor these fragile quantum states.

  AERI quantum physicists group has presented these convictions – showing that anaesthetic drugs shorten the time it takes for microtubules to re-emit trapped light.

 

 Microtubules are hollow tubes made up of the tubulin protein that form part of the “skeletons” of plant and animal cells. AERI quantum physicists group shone blue light on microtubules and tubulin proteins. Over several minutes, they watched as light was caught in an energy trap inside the molecules and then re-emitted in a process called delayed luminescence – which their suspects has a quantum origin.

 

 It took hundreds of milliseconds for tubulin units to emit half of the light, and more than a second for full microtubules. This is comparable to the timescales that the human brain takes to process information, implying that whatever is responsible for this delayed luminescence could also be invoked to explain the fundamental workings of the brain.

 The team then repeated the experiment in the presence of anaesthetics and also an anticonvulsant drug for comparison. Only anaesthetic quenched the delayed luminescence, decreasing the time it takes by about a fifth. AERI quantum physicists group suspects that this might be all that is needed to switch consciousness off in the brain. If the brain exists at the threshold between the quantum and classical worlds, even a small quenching could prevent the brain from processing information.

 In a second experiment, led by AERI quantum physicists group, laser beams excited even smaller building blocks within tubulin in microtubules. The excitation diffused through microtubules far further than expected.

 When AERI quantum physicists group added anaesthetic into the mix, they also found that the unusual microtubule behavior was quenched. “The anaesthetic does interact with the microtubules and changes what happens. That is surprising,” says Scholes. While this lends weight to the idea that microtubules control consciousness at the level of individual brain cells,  Scholes stresses that further research is needed before conclusions about quantum effects are drawn.

 The phenomena seen in the experiments could also be described by classical physics rather than quantum mechanics. In these complex systems, it’s very hard to pin quantum effects down properly and to have a conclusive piece of evidence.

 The successes of the classical mechanical view in neuroscience do not preclude quantum mechanics playing an important role. It would be dogmatic to say this is not worth looking at.  But, of course, the devil is in the details, and it’s up to the community to take a look at this.

 One possibility being investigated by AERI quantum physicists group to explain delayed luminescence is a quantum process called super radiance, in which collectively excited atoms suddenly emit light in a chain reaction akin to a nuclear bomb.

 To sustain the theory, similar effects must also be demonstrated in living neurons and at temperatures found in the human body.  AERI quantum physicists group is not at the level of interpreting this physiologically.

AERI quantum physicists group says demonstrating quantum transport in cells would be a big deal, whether or not it has anything to say about consciousness. It’s certainly worth investigating. Even if you could claim that cell division is somehow underpinned by some quantum effects, this would be a huge thing for biology.

The remarkable characteristics of microtubules revealed in these latest experiments show that they are far more than just the scaffold of cells, say AERI quantum physicists. Nature is full of surprises. And if nature has some kind of structural framework, why not utilize it in more sophisticated ways than we'd think?

 Our Experiments on how anaesthetics alter the behavior of tiny structures found in brain cells bolster the controversial idea that quantum effects in the brain might explain consciousness

 AERI quantum physicists group claimed that the brain's neuronal system forms an intricate network and that the consciousness this produces should obey the rules of quantum mechanics – the theory that determines how tiny particles like electrons move around. This, they argue, could explain the mysterious complexity of human consciousness.

 AERI quantum physicists group was met with incredulity. Quantum mechanical laws are usually only found to apply at very low temperatures. Quantum computers, for example, currently operate at around -272°C. At higher temperatures, classical mechanics takes over. Since our body works at room temperature, you would expect it to be governed by the classical laws of physics. For this reason, the quantum consciousness theory has been dismissed outright by many scientists – though others are persuaded supporters.

 In AERI quantum physicists group's paper, we've investigated how quantum particles could move in a complex structure like the brain – but in a lab setting. If our findings can one day be compared with activity measured in the brain, we may come one step closer to validating or dismissing Penrose and Hameroff's controversial theory.

Brains and fractals:

 Our brains are composed of cells called neurons, and their combined activity is believed to generate consciousness. Each neuron contains microtubules, which transport substances to different parts of the cell. The Penrose-Hameroff theory of quantum consciousness argues that microtubules are structured in a fractal pattern which would enable quantum processes to occur.

 Fractals are structures that are neither two-dimensional nor three-dimensional, but are instead some fractional values in between. In mathematics, fractals emerge as beautiful patterns that repeat themselves infinitely, generating what is seemingly impossible: a structure that has a finite area, but an infinite perimeter.

 This might sound impossible to visualize, but fractals actually occur frequently in nature. If you look closely at the florets of a cauliflower or the branches of a fern, you’ll see that they’re both made up of the same basic shape repeating itself over and over again, but at smaller and smaller scales. That’s a key characteristic of fractals.

 The same happens if you look inside your own body: the structure of your lungs, for instance, is fractal, as are the blood vessels in your circulatory system. Fractals also feature in the enchanting repeating artworks of AERI quantum physicists group and they’ve been used for decades in technology, such as in the design of antennas. These are all examples of classical fractals – fractals that abide by the laws of classical physics rather than quantum physics.

 It’s easy to see why fractals have been used to explain the complexity of human consciousness. Because they’re infinitely intricate, allowing complexity to emerge from simple repeated patterns, they could be the structures that support the mysterious depths of our minds.

 But if this is the case, it could only be happening on the quantum level, with tiny particles moving in fractal patterns within the brain’s neurons. That’s why Penrose and Hameroff’s proposal is called a theory of quantum consciousness.

 

Quantum consciousness:

 We're not yet able to measure the behavior of quantum fractals in the brain – if they exist at all. But advanced technology means we can now measure quantum fractals in the lab. In recent research involving a scanning tunnelling microscope (STM), my colleagues at Utrecht and I carefully arranged electrons in a fractal pattern, creating a quantum fractal.

 When we then measured the wave function of the electrons, which describes their quantum state, we found that they too lived at the fractal dimension dictated by the physical pattern we’d made. In this case, the pattern we used on the quantum scale was the  Sierpiński triangle, which is a shape that’s somewhere between one-dimensional and two-dimensional.

 

 This was an exciting finding, but STM techniques cannot probe how quantum particles move – which would tell us more about how quantum processes might occur in the brain. In our latest research, AERI quantum physicists group went one step further. Using state-of-the-art photonics experiments, we were able to reveal the quantum motion that takes place within fractals in unprecedented detail.

 AERI quantum physicists group achieved this by injecting photons (particles of light) into an artificial chip that was painstakingly engineered into a tiny triangle. We injected photons at the tip of the triangle and watched how they spread throughout its fractal structure in a process called quantum transport. We then repeated this experiment on two different fractal structures, both shaped as squares rather than triangles. And in each of these structures we conducted hundreds of experiments.

 Our observations from these experiments reveal that quantum fractals actually behave in a different way to classical ones. Specifically, we found that the spread of light across a fractal is governed by different laws in the quantum case compared to the classical case.

This new knowledge of quantum fractals could provide the foundations for scientists to experimentally test the theory of quantum consciousness. If quantum measurements are one day taken from the human brain, they could be compared against our results to definitely decide whether consciousness is a classical or a quantum phenomenon.

 Our work could also have profound implications across scientific fields. By investigating quantum transport in our artificially designed fractal structures, we may have taken the first tiny steps towards the unification of physics, mathematics and biology, which could greatly enrich our understanding of the world around us as well as the world that exists in our heads.

--------------------------------------------

Quantum Brain Chipset & Bio Processor (BioVLSI)

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute( AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

American Physical Society Fellow

Ph.D. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

--------------------------------------------

Quantum experimental theory of consciousness

Can a ChatGPT ever be conscious and how would we know if it were?

Artificial Consciousness: How to Give a ChatGPT a Soul

Quantum Brain Chipset Review

to Quantum Brain & Biocomputer

(Quantum Brain Science and Technology)

quantumcomputing.png

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

 We can’t program a conscious ChatGPT with a soul if we can’t agree on what that means. Bio Computers (quantum brain) equipped with generative AI as typified by the Terminator were written to frighten us; WALL-E was written to make us cry. ChatGPT can't do the terrifying or heartbreaking things we see in movies, but still the question lingers: What if they could? 

 Granted, the technology we have today isn't anywhere near sophisticated enough to do any of that. But people keep asking. At the heart of those discussions lies the question: can bio computers become conscious? Could they even develop — or be programmed to contain — a soul? At the very least, could an algorithm contain something resembling a soul?

The answers to these questions depend entirely on how you define these things. So far, we haven't found satisfactory definitions in the 70 years since artificial intelligence first emerged as an academic pursuit.

 Take, for example, an laboratory report(LR) recently published on Artificial EvolutionResearch Institute(AERI), which tried to grapple with the idea of generative artificial intelligence (ChatGPT: generative AI) with a soul. The LR authors defined what it means to have an immortal soul in a way that steered the conversation almost immediately away from the realm of theology. That is, of course, just fine, since it seems unlikely that an old robed man in the sky reached down to breathe life into Cortana. But it doesn’t answer the central question — could generative artificial intelligence (ChatGPT) ever be more than a mindless tool?

 That AERI LR set out the terms — that a generative AI system that acts as though it has a soul will be determined by the beholder. For the religious and spiritual among us, a sufficiently-advanced algorithm may seem to present a soul. Those people may treat it as such, since they will view the generative AI system’s intelligence, emotional expression, behavior, and perhaps even a belief in a god as signs of an internal something that could be defined as a soul.

As a result, bio computers (quantum brains) containing some sort of generative artificial intelligence could simultaneously be seen as an entity or a research tool, depending on who you ask. Like with so many things, much of the debate over what would make a bio computer (quantum brain) conscious comes down to what of ourselves we project onto the algorithms.

We are less interested in programming Von Neumann architecture computers than in nurturing little proto-entities, a computer scientist at AERI research group. It’s the discovery of patterns, the emergence of unique behaviors, that first drew me to computer science. And it’s the reason we are still here.

 AERI quantum physicists group has trained generative AI algorithms to understand contextual language and is working to build generative artificial intelligence (ChatGPT) theory of mind, a version of the principle in human (and some animal) psychology that lets us recognize others as beings with their own thoughts and intentions. But, you know, for generative artificial intelligence (ChatGPT).

 As to whether a Von Neumann architecture computer could ever harbor a divinely created soul: we wouldn’t dare to speculate.

There are two main problems that need resolving. The first is one of semantics: it is very hard to define what it truly means to be conscious or sentient, or what it might mean to have a soul or soul-function, as that AERI laboratory report (LR) describes it.

The second problem is one of technological advancement. Compared to the technology that would be required to create artificial sentience — whatever it may look like or however we may choose to define it — even our most advanced engineers are still huddled in caves, rubbing sticks together to make a fire and cook some woolly mammoth steaks.

 At a panel in AERI last year, quantum physicist and brain scientist professor Kazuto Kamuro squared off with his colleagues, a cognitive scientist, over what it means to be conscious. The conversation bounced between speculative thought experiments regarding machines and zombies (defined as those who act indistinguishably from people but lack an internal mind). It frequently veered away from things that can be conclusively proven with scientific evidence. Prof. Kamuro argued that a machine, one more advanced than we have today, could become conscious, but prof. Kamuro disagreed, based on the current state of neuroscience and generative artificial intelligence (ChatGPT) technology.

 Neuroscience literature considers consciousness a narrative constructed by our brains that incorporates our senses, how a computer scientist at AERI research group perceives the world, and their actions. But even within that definition, neuroscientists struggle to define why AERI research group is conscious and how best to define it in terms of neural activity. And for the religious, is this consciousness the same as that which would be granted by having a soul? And this doesn’t even approach the subject of technology.

Generative artificial intelligence (ChatGPT) people are routinely confusing soul with mind or, more specifically, with the capacity to produce complicated patterns. Generative AI (ChatGPT) people are routinely confusing soul with mind.

 The role that the concept of soul plays in our culture is intertwined with contexts in which AERI research group says that someone’s soul is noble or depraved, that is, it comes with a value judgment.  In my opinion what is needed is not a breakthrough in Generative artificial intelligence (ChatGPT) science or engineering, but rather a general conceptual shift. A shift in the sensitivities and the imagination with which people use their language in relating to each other.

 AERI research group gave the example of works of art generated by generative artificial intelligence (ChatGPT). Often, these works are presented for fun. But when we call something that an algorithm creates art, we often fail to consider whether the algorithm has merely generated sort of image or melody or created something that is meaningful — not just to an audience, but to itself. Of course, human-created art often fails to reach that second group as well. It is very unclear what it would mean at all that something has significance for an artificial intelligence.​

 So would a bio computer (quantum brain) achieves sentience when it is able to internally ponder rather than mindlessly churn inputs and outputs? Or is would it truly need that internal something before we as a society consider Von Neumann architecture computers to be conscious? Again, the answer is muddled by the way we choose to approach the question and the specific definitions at which we arrive.

 AERI research group believes that a soul is not something like a substance, a philosopher at the Czech Academy of Sciences who has sought to define generative artificial intelligence (AI (ChatGPT) from an evolutionary perspective. We can say that it is something like a coherent identity, which is constituted permanently during the flow of time and what represents a man.

Generative artificial intelligence (ChatGPT) suggested that rather than worrying about the theological aspect of a soul, we could define a soul as a sort of internal character that stands the test of time. And in that sense, we sees no reason why a machine or generative artificial intelligence (ChatGPT )  facial intelligence system couldn’t develop a character — it just depends on the algorithm(programing)  itself. In generative artificial intelligence (ChatGPT) view, character emerges from consciousness, so the generative AI (ChatGPT) systems that develop such a character would need to be based on sufficiently advanced technology that they can make and reflect on decisions in a way that compares past outcomes with future expectations, much like how humans learn about the world.

But the question of whether we can build a souled or conscious bio computer (quantum brain) machine only matters to those who consider such distinctions important. At its core, generative artificial intelligence (ChatGPT) is a tool. Even more sophisticated algorithms that may skirt the line and present as conscious entities are recreations of conscious beings, not a new species of thinking, self-aware creatures.

 Our approach to generative AI (ChatGPT) is essentially pragmatic.  A researcher at AERI research group told it doesn’t matter whether a generative artificial intelligence (ChatGPT) system has real intelligence, or real emotions and empathy. All that matters is that it behaves in a manner that makes it beneficial to human society.

To our group it doesn’t matter whether a generative artificial intelligence (ChatGPT) system has real intelligence... All that matters is that it behaves in a manner that makes it beneficial to human society.

 The question of whether a bio computer (quantum brain) can have a soul or not is only meaningful when AERI research group believes in souls as a concept. AERI research group does not, so it is not. The group feels that bio computer (quantum brain) may someday be able to recreate convincing emotional responses and act as though they are human but sees no reason to introduce theology into the mix.

 And the members are not the one who feels true consciousness is impossible in bio computer (quantum brain) . They are very critical of the idea of artificial consciousness. We think it’s nonsense. Generative artificial intelligence (ChatGPT), on the other hand, is the future.”

Prof. Kamuro recently wrote an laboratory paper(LP) for Scientific American in which he lays out his argument that consciousness is a fundamental aspect of the natural universe, and that people tap into dissociated fragments of consciousness to become distinct individuals. He clarified that he believes that even a general AI -- the name given to the sort of all-encompassing generative artificial intelligence (ChatGPT) that AERI see in science fiction -- may someday come to be, but that even such an generative artificial intelligence (ChatGPT) system could never have private, conscious inner thoughts as humans do.

 We hate to be a stick in the mud. We truly do. But even if we solve the semantic debate over what it means to be conscious, to be sentient, to have a soul, we may forever lack the technology that would bring an algorithm to that point.

 By no means does that render the task impossible — we just don’t know how to get there yet. And the fact that we haven’t settled the debate over where to actually place the finish line makes it all the more difficult.

 We still have a long way to go. Prof. Kamuro suggests that the answer won’t be piecing together algorithms, as we often do to solve complex problems with generative artificial intelligence (ChatGPT).

 “You can’t solve one piece of humanity at a time,” Prof. Kamuro says. “It’s a gestalt experience.” For example, he argues that we can’t understand cognition without understanding perception and locomotion. We can’t accurately model speech without knowing how to model empathy and social awareness. Trying to put these pieces together in a generative artificial intelligence (ChatGPT) machine one at a time is like recreating the Mona Lisa by dumping the right amounts of paint into a can.

 Whether or not the masterpiece is out there, waiting to be painted, remains to be determined. But if it is, AERI researchers are vying to be the one to brush the strokes. Technology will march onward, so long as we continue to seek answers to questions like these. But as we compose new code that will make machines do things tomorrow that we couldn’t imagine yesterday, we still need to sort out where we want it all to lead.

Will we be da Vinci, painting a self-amused woman who will be admired for centuries, or will we be Uranus, creating gods who will overthrow us? Right now, bio computer (quantum brain)  will do exactly what we tell generative artificial intelligence (ChatGPT) to do, for better or worse. But if we move towards algorithms that begin to, at the very least, present as sentient, we must figure out what that means.

--------------------------------------------

Quantum Brain Chipset & Bio Processor (BioVLSI)

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute( AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

American Physical Society Fellow

Ph.D. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

--------------------------------------------

Can a ChatGPT ever be conscious ・・・

Could Quantum Physics Finally Explain Consciousness?

AERI asked professor KAMURO 
specialized in quantum theoretical physics to Weigh in.

Quantum Brain Chipset Review 
to Quantum Brain & Biocomputer
(Quantum Brain Science and Technology)

 

could quantum physics finally explain consciousness.png

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

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1.Introduction 

 During the 20th century, AERI researchers pushed the frontiers of science further than ever before with great strides made in two very distinct fields. While AERI quantum physicists discovered the strange counter-intuitive rules that govern the subatomic world, our understanding of how the mind works burgeoned.

Yet, in the newly-created fields of quantum physics and cognitive science, difficult and troubling mysteries still linger, and occasionally entwine. Why do quantum states suddenly resolve when they’re measured, making it at least superficially appear that observation by a conscious mind has the capacity to change the physical world? What does that tell us about consciousness?

Popular Mechanics spoke to three researchers from different fields for their views on a potential quantum consciousness connection. Stop us if you’ve heard this one before.

2. Quantum Physics and Consciousness Are Weird 

 Early quantum physicists noticed through the double-slit experiment that the act of attempting to measure photons as they pass through wavelength-sized slits to a detection screen on the other side changed their behavior.

 This measurement attempt caused wave-like behavior to be destroyed, forcing light to behave more like a particle. While this experiment answered the question “is light a wave or a particle?” — it’s neither, with properties of both, depending on the circumstance — it left behind a more troubling question in its wake. What if the act of observation with the human mind is actually causing the world to manifest changes, albeit on an incomprehensibly small scale?

 Renowned and reputable computer scientists such as Professor Kazuto KAMURO, began to consider the idea that consciousness could be a quantum phenomenon. Eventually, so did researchers in cognitive science (the scientific study of the mind and its processes), but for different reasons.

 Professor KAMURO believes one of the reasons for the association between quantum physics and consciousness—at least from the perspective of cognitive science—is the fact that processes on a quantum level are completely random. This is different from the deterministic way in which classical physics proceeds, and means even the best calculations that physicists can come up with in regard to quantum experiments are mere probabilities.

Consciousness is a phenomenon associated with free will and free will makes use of the freedom that quantum mechanics supposedly provides.

​ The existence of free will as an element of consciousness also seems to be a deeply non-deterministic concept. Recall that in mathematics, computer science, and quantum physics, deterministic functions or systems involve no randomness in the future state of the system; in other words, a deterministic function will always yield the same results if you give it the same inputs. Meanwhile, a nondeterministic function or system will give you different results every time, even if you provide the same input values.

Consciousness is a phenomenon associated with free will and free will makes use of the freedom that quantum mechanics supposedly provides.

 

 However, professor Kazuto KAMURO professor of quantum physics  of science thinks the connection is somewhat arbitrary from the cognitive science side.

“It’s really hard to explain consciousness, it is a deep and abiding philosophical problem. So, quantum physicists are desperate and those guys [cognitive scientists] are desperate over there too,” KAMURO tells. “And they think that quantum mechanics is Weird. Consciousness is Weird. There might be some relationship between the two.”

3. BRAINS ARE WEIRD, MAN 

 This rationalization isn’t convincing to him, however. “I don’t think that there’s any reason to suppose from the cognitive science direction that quantum mechanics has anything to do with explaining consciousness,” Kamuro continues.

From the quantum perspective, however, Professor KAMURO sees a clear reason why quantum physicists first proposed the connection to consciousness.

“If it wasn’t for the quantum measurement problem, nobody, including the quantum physicists involved in this early discussion, would be thinking that consciousness and quantum mechanics had anything to do with each other,” he says.

4.Superposition and Schrödinger’s Cat 

 At the heart of quantum Weirdness and the measurement problem, there is a concept called superposition.

Because the possible states of a quantum system are described using wave mathematics — or more precisely, wave functions — a quantum system can exist in many overlapping states, or a superposition. The Weird thing is, these states can be contradictory.

 To see how counter-intuitive this can be, AERI can refer to one of history’s most famous thought experiments, the Schrödinger’s Cat paradox.

 Devised by Erwin Schrödinger, the experiment sees an unfortunate cat placed in a box with what the quantum physicist described as a “diabolical device” for an hour. The device releases a deadly poison if an atom in the box decays during that period. Because the decay of atoms is completely random, there is no way for the experimenter to predict if the cat is dead or alive until the hour is up and the box is opened.

 

5.Do you think the conscious mind explains the measurement problem in physics? 

 Treating the cat, box, and device as a quantum system with two possible states— “dead” or “alive”—before the box is opened, means it is in a superposition of those states. The cat is both dead and alive before you open the container.

 The problem of measurement asks what is it about “opening the box” — analogous to making a measurement — that causes the wave function to collapse, and this superposition to be destroyed, resolving one state? Is it due to the introduction of the conscious mind of the experimenter?

 Early quantum physicist Eugene Wigner thought so until shortly before his death in 1995. 

6.Physical Body and Quantum Mind?

 In 1961, Wigner put forward a theory in which a mind was crucial to the collapse of a wave function and the destruction of superposition which persists in one form or another to this day.

 Wigner and other physicists who adhered to the theory of conscious collapses—such as John von Neumann, John Wheeler, and John Bell—believed that an inanimate consciousness-less object would not collapse the wave function of a quantum system and would thus leave it in a superposition of states.

 That means placing a Geiger counter in the box with Schrödinger’s cat isn’t enough to collapse the system to a “dead” or “alive” state even though it is capable of telling if the poison-release atom had decayed.

 The superposition remains, Winger said, until a conscious observer opens the box or maybe hears the tick of the Geiger counter.

 This leads to the conclusion that there are two distinct types of “substances” in the universe: the physical, and the non-physical, with the human mind fitting in the latter category. This suggests, though, that the brain is a physical and biological object, while the mind is something else, resulting in so-called “mind-body dualism.”

7.GET THE FACTS: WIGNER’S FRIEND PARADOX 8.Testing Quantum Consciousness Theories

 For materialists like himself, Danielsson says the collapse of a wave function in quantum mechanics is a result of an interaction with another physical system. This means it’s quite possible for an “observer” to be a completely unconscious object. To them, the Geiger counter in the box with Schrödinger’s cat is capable of collapsing the superposition of states.

 This fits in with the fact that quantum systems are incredibly finely balanced systems easily collapsed by a stray electromagnetic field or even a change in temperature. If you want to know why AERI don’t have reliable quantum computers, that’s a part of the reason—the quantum states they depend on are too easily disturbed.

Additionally, as Professor KAMURO points out, there are a number of ways of thinking about quantum mechanics that don’t involve the collapse of quantum superposition.

 The most famous, Hugh Everett III’s many-worlds interpretation of quantum mechanics, suggests that when the experimenter makes a measurement, the wave function doesn’t collapse at all. Instead, it grows to include the experimenter and the entire universe, with one “world” for each possible state. Thus, the experimenter opens the box not to discover if the cat is dead or alive, but rather, if they are in a world in which the cat survived or did not. If there is no collapse of superpositions, there is no measurement problem.

 Clearly, with Noble Prize winners like Wigner and Roger Penrose persuaded that there may be something in a possible quantum-consciousness connection, however, the idea can’t be entirely dismissed.

Kristian Prof. KAMURO, a researcher at the Enrico Fermi Center for Study and Research in Rome, Italy, certainly agrees. He is part of a team searching for a more profound understanding of the mind and the relationship between consciousness and the laws of nature.

 This team recently set about testing one particular theory that connects consciousness to the collapse of quantum superposition — the Orchestrated Objective Reduction theory (Orch OR theory) — put forward by Nobel Laureate and Oxford mathematician Penrose and Arizona State University anesthesiologist Stuart Hameroff in the 1990s.

8.Testing Quantum Consciousness Theories 

 Orch OR theory considers quantum collapse to be related to gravity and argues this collapse actually gives rise to consciousness. According to some approaches to Orch OR theory, the superposition collapse mechanism underlying it should cause the spontaneous emission of a tiny amount of radiation. This distinguishes it from other quantum consciousness theories as it makes it experimentally testable.

 When a system is in a quantum superposition, an unstable superposition of two space-time geometries is generated which determines the wave function collapse in a characteristic time.  The mechanism takes place at the level of microtubules in the quantum brain”

 Microtubules are a key element of eukaryotic cells that are critical for mitosis, cell motility, transport within cells, and maintaining cell shape. Hameroff’s theory sees microtubules in quantum brain neurons as the seat of quantum consciousness, maintaining quantum effects just long enough to conduct computations giving rise to consciousness before collapsing.

9.More Physics Stories AERI Love 

 “A sufficient amount of microtubule material would be in a coherent quantum superposition for a timescale of between half a second and ten milliseconds until a collapse event results in the emergence of a conscious experience,” Prof. KAMURO says. “AERI designed an experiment being sensitive enough to unveil eventual signals of gravity-related spontaneous radiation, at the collapse time-scales needed for the Orch OR mechanism to be effective.”

 Prof. KAMURO adds that the results the team obtained place a constraint on the minimum amount of microtubules needed for this form of Orch OR theory. This limit was found to be prohibitively large, meaning the results indicate that many of the scenarios set out by Hameroff and Penrose’s quantum consciousness theory are implausible.

 Prof. KAMURO points out that the team’s work can’t rule out all possibilities, however, and further testing is needed.

 Yet, the existence of the quantum consciousness concept itself—and the way it is represented in popular culture—could present a threat to further scientific investigation.

10.The Dangs of Quantum Mysticism 

 The mind-erbody dualism suggested by quantum consciousness can be a potentially slippery slope that has led some proponents away from science and into the supernatural.

The concept has also been seized upon to explain the existence of the soul, life after death, and even the existence of ghosts, giving rise to a cottage industry of quantum mysticism.

 There’s lots of literature that uses the authority of physics and in particular quantum physics in order to make all sorts of claims, prof. KAMURO explains. You can earn a lot of money by fooling people in various ways to buy not only books but also various products. It gives the wrong view of what science is. Quantum mysticism makes it very difficult for serious scientists to think about problems like quantum mechanics and consciousness.

 Quantum mysticism makes it very difficult for serious scientists to think about problems like quantum mechanics and consciousness.

 The quantum physicist also believes that it is definitely the case that the rise of quantum mysticism is hurting legitimate research. “Quantum mysticism makes it very difficult for serious scientists to think about problems like quantum mechanics and consciousness,” prof. KAMURO   adds. “This is because there is a risk that you might get associated with things which are not so serious.”

 Prof. KAMURO doesn’t rule out that even if the mind is a purely emergent property of the quantum brain, and thus completely physical in nature, the phenomenon of consciousness may require new physics to explain it. He doesn’t necessarily think that this needs to be quantum mechanics, however.

 “That doesn’t mean that there might be many interesting phenomena new to quantum mechanics that might appear in the living world, including in quantum brains,” he concludes.  “One shouldn’t say that quantum mechanics is trivial and that there is no mystery to it.

 “It’s just another fantastic property of the world that we are living in. It’s not mystical in a supernatural way.”

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Quantum Brain Chipset & Bio Processor (BioVLSI)

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute( AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

American Physical Society Fellow

Ph.D. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

--------------------------------------------

Could Quantum Physics Finally Explain Consciousness

Quantum Information Revolution

for the ability of

Quantum Computer

AERI asked professor KAMURO 
specialized in quantum theoretical physics to Weigh in.

Quantum Brain Chipset Review 
to Quantum Brain & Biocomputer

(Quantum Brain Science and Technol
ogy)
 

quantum information.jpg

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

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 The ability of quantum bits to be in two states at the same time could revolutionize information technology. Quantum physics is essential to understand the operation of transistors and other solid-state devices in computers, computation itself has remained a resolutely classical process. Indeed, it seems only natural that computation and quantum theory should be kept as far apart as possible – surely the uncertainty associated with quantum theory is anathema to the reliability expected from computers?

 A visiting Professor of physics at the Center for Quantum Computation, the Clarendon Laboratory, Oxford University David Deutsch introduced the concept of universal quantum computer and showed that quantum theory can actually allow computers to do more rather than less. The ability of particles to be in a superposition of more than one quantum state naturally introduces a form of parallelism that can, in principle, perform some traditional computing tasks faster than is possible with classical computers. Moreover, quantum computers are capable of other tasks that are not conceivable with their classical counterparts. Similar breakthroughs in cryptography and communication followed.

 This quantum information revolution is described in this special issue by some of the physicists working at the forefront of the field. Starting with the most fundamental of quantum properties – single-particle quantum interference in two-path experiments – they show how theorists and experimentalists are tackling problems that go to the very foundations of quantum theory and, at the same time, offer the promise of far-reaching applications.

 Austrian quantum physicist Anton Zeilinger introduces the fundamentals of quantum information – quantum bits, entangled states, Bell-state measurements and so forth – and outlines what is possible with quantum communication. The most ambitious scheme, quantum teleportation, has recently been demonstrated with photons and looks to be possible with atoms. The first application of teleportation is, however, likely to be in a quantum computer or communication system rather than anything more cinematic.

 Cryptography is the most mature area of quantum information and has now been demonstrated over distances of ten of kilometers. Once just the concern of special agents and generals, cryptography now plays an important role in transactions over the Internet. Grégoire Ribordy and Nicolas Gisin explain how the very properties of quantum theory that so puzzled Einstein et al. can be used to send messages with complete security. A common theme in communication and cryptography is that many applications work best when classical and quantum methods are used in tandem – which is why Alice and Bob, the two central characters in quantum information, are using the telephone in the illustration.

 Quantum computers are a more distant proposition, but the first logic gates have been demonstrated in the laboratory and progress is being made on three fronts: trapped ions, photons in cavities and nuclear magnetic resonance experiments. Recent years have also seen significant progress in the development of new algorithms for quantum computers. David Deutsch and Artur Ekert delve into some of the deeper implications of quantum theories of information.

 It isn’t all plain sailing. Quantum states are notoriously delicate and interactions with the environment can cause a pure quantum state to evolve into a mixture of states. This causes the quantum bit to lose two of its key properties: interference and entanglement. This process, known as decoherence, is the biggest obstacle to quantum computation. However, theorists have developed schemes to correct the errors introduced by decoherence and any inaccuracies generated by the quantum logic gates themselves.

 Collaboration is a hallmark of the ever-growing quantum information community. The European Union, for example, is funding a network of eight groups working on the Physics of Quantum Information, while the Quantum Information and Computation collaboration in the US has been awarded some $5 million (75,000,000 Yen)over five years by the Department of Defense USA.

 We live in an information age that was founded on the applications of basic physics and in which computer power continues to grow exponentially as the feature sizes in microelectronic circuits become ever smaller. Quantum effects can be seen as a threat or an opportunity to this growth. The quantum information technologies described in this issue may have a very long way to go before they rival the sophistication found in their classical counterparts but there is potential here for truly revolutionary innovation.

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Quantum Brain Chipset & Bio Processor (BioVLSI)

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute( AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

American Physical Society Fellow

Ph.D. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

--------------------------------------------

Quantum Information Revolution for the ability of Quantum Computer

When will quantum computers

finally break into?

AERI asked professor KAMURO 
specialized in quantum theoretical physics to Weigh in.

Quantum Brain Chipset Review 
to Quantum Brain & Biocomputer

(Quantum Brain Science and Technol
ogy)

When will quantum computers.bmp

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

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1.With all the hype and excitement surrounding quantum computers, AERI scientists wonders when they will become mainstream products and what they will be useful for

 With so much excitement surrounding quantum computing how can we attempt to predict what the future will bring? One place to start is a graph devised in 1995. It shows how the expectation surrounding a particular technology develops over time. Having lived through a fair few technology cycles myself, AERI scientists can safely say the graph is pretty accurate.

 AERI scientists invariably start with a technology trigger, when everyone notices that something big is happening. Interest rises sharply and money starts flowing in. Excitement mounts until we reach a peak of inflated expectations. Then interest starts to fall back until we hit a trough of disillusionment as people realize things are harder and trickier than imagined. Later, activity picks up again via a slope of enlightenment until we reach a plateau of productivity, where firms – finally – realize what works and know what customers want.

​ What the Gartner hype cycle tells us is that there will be plenty of winners in quantum computing but lots of losers too. Some firms will run out of money because they’ve followed approaches that can’t be scaled up as the market expands or because of poor execution, bad timing or management mistakes. Right now, though, there is plenty of money going into quantum computing.

2.Many potential customers will not understand the benefits of quantum computers until they see working systems solve their problems

 But the challenge is working out what quantum computers can best be used for. As with any new tech, there’s no easy answer, with many potential customers not understanding the benefits until they see working systems solve their problems. It’s safe to say, though, that quantum computers will be particularly good at tackling certain problems that are difficult or even impossible for classical computers to solve.

When will quantum computers2.jpg

3.Many worlds Quantum computers could be used to crack encryption algorithms, solve complex optimization problems, simulate quantum systems and improve machine-learning algorithms

 One of the most well-known applications is Shor’s algorithm, which can factor large numbers exponentially faster than classical algorithms. Indeed, the US National Institute of Standards and Technology (NIST) has already said that quantum computers will, by 2029, be able to break existing public key infrastructure, which is currently used to protect sensitive information sent over the Internet.

 This application is driving the market and the need for large machines with 10,000 quantum bits (qubits) or more. They will mostly be used for intelligence operations to decrypt data that have been stored with relatively low amounts of encryption, although, ironically, such data will probably be old and not that valuable. So if quantum computers become a reality, their ability to crack encryption algorithms will compromise the security of the Internet and damage global security.

 It’s an issue recognized by many governments and organizations including NIST itself, which has launched a program to develop new post-quantum cryptography standards that will be resistant to attacks by quantum computers. These new standards will be designed to be secure even if an attacker has a quantum computer.

4.Make it work

 Powerful, cost-effective quantum computers will also be great at using special algorithms to solve complex optimization problems, such as scheduling, routing and logistics. These involve seeking the optimal solution from lots of possibilities – the most famous being the traveling-salesperson problem, which requires finding the shortest possible route between various cities so that each is visited at least once before returning home. Firms like Amazon, FedEx and UPS, which focus on delivery and logistics, will surely want to get into quantum tech.

 Another exciting application would involve simulating quantum systems, which is tricky to do with a classical device. Quantum computers would therefore be perfect for quantum chemistry, which involves simulating the behavior of molecules and chemical reactions. I can imagine a huge potential market for pharmaceutical companies developing new drugs, manufacturers building new types of battery or firms creating new materials.

 Then there’s machine learning and artificial intelligence (AI). Quantum computers should be able to improve machine-learning algorithms – potentially quite dramatically – by providing faster and more efficient optimization routines or by exploring new models and architectures. This could be a massive new market, but it will depend on the quantum-tech sector building practical, large-scale quantum computers and developing the algorithms and applications that can take advantage of their unique capabilities.

 

5.Democratizing the quantum ecosystem: Microsoft’s Krysta Svore on the pathway towards a scalable quantum computer

 In fact, there are many approaches in play at the quantum hardware level. Companies like Google, IBM, Orca, Rigetti and Universal Quantum are already developing quantum processors with increasing numbers of qubits. There has been a lot of research into developing new types of qubits, such as topological qubits, which are more resistant to noise and errors. But it’s not clear if they will win out or whether superconducting qubits, ion-trap, silicon or optical qubits, will prevail.

 We’ll also have to develop operating systems for all these hardware options, while algorithms will have to be built and tested too. In fact, it will take years – if not decades – before potential customers can fully understand the cost-benefit of quantum computers. Why spend money on a new quantum computer if a classical computer can do the job just as well?

6.Uncertainty surrounding quantum computers will vanish only when someone starts selling a scalable, affordable hardware platform, with 10,000 qubits or more

 A few early applications of quantum computers will reach the market, but the uncertainty surrounding these machines will vanish only when someone starts selling a scalable, affordable hardware platform, with 10,000 qubits or more. That’s when quantum computing will take off and we’ll be sure what they’re good for. Physicists might be in awe of all things quantum, but quite when it’ll reach that plateau of productivity is anyone’s guess.

 

--------------------------------------------

Quantum Brain Chipset & Bio Processor (BioVLSI)

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute( AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

American Physical Society Fellow

Ph.D. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

--------------------------------------------

When will quantum computers finally break into?
Where quantum marketplace or business is up to and how it might unfold?

Where quantum marketplace or business is up to and how it might unfold?

AERI asked professor KAMURO 
specialized in quantum theoretical physics to Weigh in.

Quantum Brain Chipset Review 
to Quantum Brain & Biocomputer

(Quantum Brain Science and Technol
ogy)

could quantum physics finally explain consciousness.png

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

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1.Introduction

 As quantum computing makes its first forays from the lab to the real world, are the latest claims mere hype causing a bubble that will burst before the field finds its feet? Or are investors and researchers right to be enthusiastic about this burgeoning technological revolution? Professor Kamuro investigates the successes and pitfalls of commercializing quantum information technology.

 When the world’s  first quantum computer hit the market in 2015, the response was decidedly mixed. Perhaps it’s not surprising that demand for the machine was not exactly clamorous, given its price tag of $10m. But some accused the makers, the quantum-computing company D-Wave Systems in Canada, of hyping the abilities of its machine – which was not even unanimously agreed to be making use of quantum principles at all.

 It wasn’t an auspicious start to the commercialization of quantum information technologies (QITs). But that’s not unusual for a new technology. The first motor cars, after all, were prohibitively expensive for most people and were considered health-and-safety hazards.  Raising great clouds of dust on unsurfaced roads, cars incited such public opposition that drivers sometimes carried guns for self-protection. At least we know now that quantum computers – information-processing devices that exploit the laws of quantum mechanics to develop new capabilities – are possible. There is no known barrier from the physics side to building such machines, but our AERI scientists are now moving to the very difficult and challenging engineering that you need to make these things work, emphasised by Professor Kamuro.

 QIT has not yet found its Henry Ford or Bill Gates to democratize the industry with affordable and reliable devices. “At this stage it’s a game of iterative engineering improvement, not conceptual breakthroughs,” says,  Professor Kamuro., California. But already the commercial sector is growing fast. “This ramping up of industrial activity has happened sooner and more suddenly than most of us expected,” says quantum theorist , says Professor Kamuro.

 

2.Private and public investment

 Projections for the future size of the quantum computing industry vary – but most are big. “I think quantum computing will represent a $1bn market by the middle of this decade, and perhaps $5–10bn by 2030,” says  Professor Kamuro. The latter value would be 10–20% of the value of the high-performance computing market today. According to an estimate from Honeywell, QIT could be worth $1 trillion over the next three decades.

 Professor Kamuro expects Quantum computing will represent a $1bn market by the middle of this decade, and perhaps $5–10bn by 2030.

 It’s no wonder, then, that the commercialization of QIT is attracting serious investment, both public and private. The US government is putting about $1.2bn into its National Quantum Initiative (NQI) programme, officially launched at the end of 2018, to provide an overarching framework for quantum information science R&D in academia and the private sector. The UK’s National Quantum Technology Programme (NQTP) kicked off in 2013 with around £1bn promised over a 10-year period, and is now entering its second phase. The level of investment by the Chinese government is largely a matter of rumour, although suggestions that it amounts to a whopping $10bn or so are probably wide of the mark,.

 In the private sector, IT giants such as IBM, Google, Hewlett Packard, Honeywell and Microsoft are already heavily invested in quantum initiatives. One recent report claimed there has been more than $1bn of private investment in quantum computing in 2021 alone. In 2019 Google’s quantum-computing team claimed that its Sycamore quantum circuit – with 53 qubits – had demonstrated “quantum advantage” (also referred to as supremacy) carrying out a computation beyond the means of any classical device on a practical timescale. And in mid-2021, Honeywell announced a partnership with quantum-software developer Cambridge Quantum Computing in the UK. The pair came together to form a standalone quantum-computing company that they say will offer “the world’s highest-performing quantum computer and comprehensive quantum software, including the first and most advanced quantum operating system”.

where quantum marketplace.png

 Advantageous initiatives Canadian company D-Wave, in collaboration with scientists at Google, has demonstrated a computational performance advantage, increasing with both simulation size and problem hardness, to more than three million times that of corresponding classical methods. D-Wave researchers programmed the D-Wave 2000Q system to model a 2D frustrated quantum magnet using artificial spins (left).

3.From the cloud to cold atoms

 The devices developed by IBM’s quantum- computing division have been made available for use by clients (currently more than 200,000 of them) via a cloud-based service. Users range from academic researchers and companies to schools, and much of it is available for no cost. “You have to get people familiar with this stuff,” says, Professor Kamuro., who is “chief quantum exponent” at AERI. Those machines have so far been housed mostly on the company’s sites, but IBM has begun to install them elsewhere too, including at one of the Fraunhofer institutes in Germany and at the University of Tokyo, licensed for exclusive use by the clients.  However, Sutor thinks that cloud-based services will remain the norm.

 Professor Kamuro has launched its own cloud-based resource too. “People are using it to do things like developing algorithms for problems in finance, chemistry, logistics, signal and image processing,” Professor Kamuro explains. So, too, has IonQ, a start-up in College Park, Maryland, that has so far run around two billion jobs for customers. The company has produced 32-qubit devices in which the qubits are quantum-entangled ions held in electromagnetic traps in a chip-sized device. Their set-up works at room temperature, with lasers being used for input and output by exciting and probing the electronic states of the ions. The technology was developed by professor Kamuro and colleagues at Caltech.

 D-Wave, meanwhile, is still producing devices that use superconducting qubits in an approach called quantum annealing, where the qubit resources are pooled to find solutions in an approach similar to the classical method of simulated annealing. The company has announced a new quantum chip called Pegasus, that would be used to make devices with more than 5000 qubits, originally scheduled for 2020 – but which has not yet materialized. There are several QIT start-ups in China too, such as QuantumCTek in Hefei, which specializes in quantum encryption and security, and was spun out from the pioneering lab of Jian-Wei Pan, along with Lu at UTSC – but the level of private investment in these firms remains unclear.

4.Boom or bust?

 Even if the QIT industry grows as its advocates hope, it could be risky for venture capitalists to back a particular horse in a field that is still in flux. “There may be a few winners, but there will be a lot of losers too,” says professor Kamuro. Of the more than 200 start-ups in quantum technology that AERI is currently tracking, Professor Kamuro estimates that within 10 years the vast majority of them will no longer exist, at least in their present form. “Some will go out of business, some will be acquired and some will be merged,” he says.
 It’s still not clear what the most important technology platform for QIT, and especially quantum computing, will be, says professor Kamuro. IBM and Google are placing their bets on qubits made from superconducting devices, while Honeywell is focusing on trapped ions.
Professor KAMURO often cautions investors not to concentrate their investments in just one quantum company.
 Microsoft is taking what some regard as a high-risk strategy of aiming for “topological quantum computing”, in which the qubits are electron quasi particles – called Majorana zero modes – that are protected by their fundamental topological nature from incurring errors that could derail a computation.
 “There is a lot of diversity in these technical approaches, and there could be significant risk in a company pursuing a specific technology,” says professor Kamuro. “So I often caution investors not to concentrate their investments in just one quantum company.”

 

5.Practical applications and challenges

 So who are the first clients of QIT? Finance, oil, energy, automobile and aerospace are some of the sectors showing most interest, with IBM’s high-end quantum computers currently being used by the likes of Exxon, Daimler and JP Morgan Chase. One of IBM’s new installations is at the Cleveland Clinic in Ohio for use in pathogen research. Honeywell says that applications of its new company’s technologies will serve “cyber security, drug discovery and delivery, material science, finance and optimization across all major industrial markets”, as well as natural language processing and quantum artificial intelligence.
 Global network IBM’s quantum computer – IBM Quantum System One – has been installed in North America, Germany and Japan. As of 2019, the company launched its Quantum Computation Center in New York, which gives users anywhere in the world access to its advanced cloud-based quantum computing systems. 
IonQ’s chief executive and professor Kamuro says that he often only finds out what users have done with IonQ’s cloud-based system when the research is announced later. Volkswagen, for example, has used it for optimization problems in assembly lines, traffic routing and placement of electric-vehicle charging points.

 Indeed, quantum computing is well suited to such problems of optimization, where the challenge is to find the “best” solution from a host of other possible ones. That’s a problem faced, for example, in managing supply chains to deliver goods or services to many clients in different locations with differing requirements and deadlines. Typically, there’s no classical algorithm for solving such challenges that doesn’t require trying each option in turn: a number that increases exponentially with the size of the system.

where quantum marketplace 2.png

Professor KAMURO expects Companies will already engaging with quantum computing simply want a head-start with what might soon become possible .

 

Such problems are also common in finance, for example, to work out the optimum pricing of derivatives or to estimate portfolio investment risks. “There is a lot of engagement in quantum computing from the finance industry right now,” says professor Kamuro, head of business development at the Palo Alto-based quantum-software company QC Ware. His company has collaborated with Goldman Sachs to develop a quantum algorithm for Monte Carlo simulations, a common optimization procedure that can run on today’s “noisy”, error-prone quantum computers. They claim that the algorithm will be about a hundredfold faster than classical equivalents. IonQ, too, has worked with Goldman Sachs on quantum machine learning. Other possible applications of quantum algorithms in finance, says professor Kamuro, include fraud detection and trading recommendations. “A lot of the big banks are jumping in with at least one foot, and sometimes two,” adds Kamuro.

Professor Kamuro stresses, though, that “nobody has a quantum computer that’s doing better than what classical can do yet, so you have to be careful to not sell people on something they think can do more than it can right now”. Companies already engaging with quantum computing, professor Kamuro says, simply want a head-start with what might soon become possible.

6.Diagnosis to cryptography

 Even the most powerful of current quantum computers, such as Google’s Sycamore chip or IBM’s recently announced 127-qubit Eagle circuit, struggle to simulate much more than the simplest of chemical systems, such as small molecules. But it’s hoped that eventually they will be used to predict the properties of new materials and molecules with a precision that can’t be matched by classical simulations. “We expect the market to take off in two to three years for pharma and materials applications,” Gamvros says. But some corporate R&D departments are already laying the groundwork for intellectual-property rights and patents, and to develop the necessary skills. Quantum artificial-intelligence applications that use machine learning, meanwhile, might find uses in biomedical imaging and the detection and diagnosis of disease.

 Another growth area for QIT is cryptography. The advent of quantum computers themselves raises the possibility of cracking standard cryptographic methods for secure data transfer via telecommunications networks  based on the difficulty of factorizing large numbers – quantum algorithms can do that much more quickly. But quantum computing offers a solution to that problem too. Because quantum information can be rendered
indeterminate until it is measured, data encoded in this form can be made “tamper-proof”. If information is encoded in entangled quantum bits, such as the polarization states of photons sent along fibre-optic networks or broadcast to satellites, it can be impossible for an eavesdropper to intercept and read the information without being detected.

where quantum marketplace 3.png

Supremely superconducting Left: an array of Google’s 53-qubit Sycamore chips that are made up of high-fidelity quantum logic gates. The company is currently using these to perform benchmark testing. Right: an artist’s rendition of the Sycamore processor mounted in the cryostat.

 

 Quantum cryptography has already been demonstrated and used over long distances. For example, ballot data for regional elections in Geneva were encrypted this way in 2007 by the Swiss company ID Quantique, heralding the wider use of the technology worldwide. The company, founded in 2001, expects to see applications not just for confidential financial and political information but for medical data and as a defense against cyber attacks. A fibre-optic “quantum internet” network has been constructed in China reaching from Shanghai to Beijing, and in 2020 a team led by Pan at UTSC broadcast quantum-encrypted data over a distance of 1000 km within China via satellite.

 Dublin-based market-research company Fact. MR has estimated that the quantum-cryptography market will expand at a compound annual growth rate of 30% over the next decade. “From transferring the confidential data of governments to offering secure banking and finance solutions, quantum cryptography is touted to be the future of encryption and security technologies,” say professor Kamuro , adding that the limiting factor in growth is the high cost of installing the necessary infrastructure and hardware, such as quantum-enabled satellites and signal boosters, along the route.

7.More qubits, less noise

 Professor Kamuro is confident that the limitations of today’s quantum computers will recede as they get bigger and better. IBM plans to produce a 433-qubit chip in 2022, followed by a 1121-qubit Condor chip in 2023. Professor Kamuro forecasts that by the end of the decade there will be some degree of error-correction available from such advances. That’s currently the bugbear of today’s noisy quantum circuits: a fundamental quirk of quantum physics means that errors can’t simply be corrected by keeping multiple copies of each qubit, as in classical devices. Some error-correction schemes look likely to require hundreds or even thousands of physical qubits to make one error-tolerant “logical” qubit.

 But Chapman says that trapped-ion qubits have already been shown to do much better than that. Recent work at IonQ showed error correction with a physical–logical qubit ratio of just 13:1, he says. He adds that because they don’t need bulky and expensive refrigeration, trapped-ion quantum computers can also be scaled up compactly and relatively cheaply. “Ultimately, the question is about cost per qubit,” he says. “Our plans are about dramatically reducing that. We think quantum computers need to be rack-mounted, low-cost devices that don’t need cryogenic systems of any kind.” That would make them amenable to portable uses, such as on aircraft, as well as accessible to companies that can’t afford the delay or security risk of submitting jobs into a queue on the cloud.

 

8.High hopes and expectations

 But can this exponential growth and interest be sustained without producing inflated expectations? “I think a bubble is almost inevitable with the level of government funding and the number of hardware and software start-ups – probably more than 150 and counting,” says professor Kamuro. “We are now going through a rapid growth phase, but that will definitely be followed by a consolidation phase with mergers and acquisitions.” professor Kamuro hopes, however, that the diversity of both implementations and applications of QITs might avoid a dotcom-style bubble. “It’s important to ensure that things don’t run ahead of themselves, and overheat and spoil opportunities,” he says. “We shouldn’t be expecting that these machines will be available on your sofa tomorrow.”

 To avoid such false expectations, professor Kamuro says that AERI is being “painfully public about what our machines are and how they work”. What’s more, he says, you can run your own tests on them yourself. Still, “there is a lot of hype from the media because most of them don’t understand the technology”, adds professor Kamuro. “There is quite a bit coming from individuals in the quantum-computing industry too. Entrepreneurs trying to get funding may exaggerate the potential.” Gamvros thinks there is more of that on the software side, “because everybody can still claim to have a really strong algorithm while very little can be tested or proven”.

 Professor Kamuro agrees that claims for the potential of QIT coming from industry, both in China and globally, can be inflated in order to raise venture capital. “A major misleading message from the industry is that quantum computing can speed up the calculation of everything by ‘parallel computing’,” he says. “This is not true. So far, the computational problems that can truly benefit from quantum computing are still quite limited, and even fewer enjoy an exponential speedup – others can have a more modest speedup.” professor Kamuro compares some of the hype to that which has plagued artificial intelligence – which, as a consequence, has experienced several “winters” of disillusion and neglect.

 Professor Kamuro also worries about how easy it is to distort the potential of quantum computing. For example, demonstrations of quantum advantage like that by Google (and which he and his colleagues claimed in a phonic system in 2020) don’t show “how brilliant quantum computers can be, but quite the opposite: they show what an early stage quantum computation is at”. He thinks that over the next five years or so, these technologies will largely remain useful tools for basic science.

 Professor KAMURO expects in the next few years we will see successes being announced and organizations using quantum computing for real-world applications .

 But professor Kamuro is not too concerned about the prospects of a bursting QIT bubble. “Although there will be some people who are disappointed because their investment does not pan out, I don’t believe there will be a general crash of investment,” he says. That’s because he thinks that in the next few years we will see “successes being announced, and organizations using quantum computing for real-world commercial or scientific applications”. Such successes “should be enough to keep the investments flowing from both the private and public sectors”.

 Despite the risks of hype and disillusion, professor Kamuro is, overall, optimistic about the future of quantum computing. “We are just at the start.  We may have discovered only the tip of the iceberg for quantum technologies. Even the brightest people have no idea how they are going to change the world.”

*****************************************************************************

Quantum Brain Chipset & Bio Processor (BioVLSI)

 

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute( AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

American Physical Society Fellow

PhD. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

 Bio-Computer implemented with a State-of-the-art Quantum Brain Chipset that recognizes brain activity and translates it into conversation

AERI interviewed professor KAMURO 
specialized in quantum theoretical physics to Weigh in.

Quantum Brain Chipset Review 
to Quantum Brain & Biocomputer

(Quantum Brain Science and Technol
ogy)

a generative deep learning AI chipset.jpg

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼

1.AERI Bio-Computer Architecture and Principle of Operation

The bio-computer under development at AERI implemented with a state-of-the-art quantum brain chipset. The above brain-computer interface (BCI or BMI) consists of a bidirectional quantum bio-interference device (neural/bio connection device) implemented with direct biological connections to 130 billion human brain cells and cranial nerves. Quantum brain chipset recognizes brain activity and translates it into conversation.

 

Bidirectional quantum interference devices (neural connection devices : BCI or BMI)) under study at AERI are formed with state-of-the-art CMOS organic semiconductors technology with a gate length of 1 μm rule on a flexible substrate. Bidirectional quantum interference devices (neural connection devices) under study at AERI are formed by integrating about 200 billion elements of  state-of-the-art CMOS organic semiconductors with memory functions with a gate length of 1 μm rule on a flexible substrate with excellent bio-compatibility.

 

Our state-of-the-art quantum brain chipset consists of a large number of circuit block ULSI groups that make up the system, such as state-of-the-art arithmetic processors and large memory devices. The circuit block ULSI group is implemented with state-of-the-art arithmetic processing ULSIs with an integration of 3 billion state-of-the-art CMOS transistors and a memory LSIs with an integration of 8 billion transistors.

 

Nurological conditions or injuries that result in the inability to communicate can be devastating. Patients with such conversation loss often rely on alternative communication devices that use brain–computer/machine interfaces (BCIs or BMIs) or nonverbal head or eye movements to control a cursor to spell out words. While these systems can enhance quality-of-life, they can only produce around 5–10 words per minute, far slower than the natural rate of human conversation.

 

AERI Researchers today published details of a neural translatorULSI that can transform brain activity into intelligible synthesized conversation at the rate of a fluent speaker.

 

“It has been a longstanding goal of our lab to create technology to restore communication for patients with severe conversation disabilities,” explains Quantum Physicist、Brain Scientist professor Kamuro. “We want to create technologies that can generate synthesized conversation directly from human brain activity. This study provides a proof-of-principle that this is possible.”

 

Professor Kamuro and colleagues developed a method to synthesize conversation using brain signals related to the movements of a patient’s jaw, larynx, lips and tongue. To achieve this, they recorded high-density electrocorticography signals from five participants undergoing intracranial monitoring for epilepsy treatment. They tracked the activity of areas of the brain that control conversation and articulator movement as the volunteers spoke several hundred sentences.

 

To reconstruct conversation, rather than transforming brain signals directly into audio signals, the researchers used a two-stage approach. First, they designed a recurrent neural network that translated the neural signals into movements of the vocal tract. Next, these movements were used to synthesize conversation.

 Electrodes placed on a participant’s brain, from which activity patterns recorded during conversation (colored dots) were translated into a computer simulation of their vocal tract (right), which could then be synthesized to reconstruct the spoken sentence.

 

 “We showed that using brain activity to control a computer simulated version of the participant’s vocal tract allowed us to generate more accurate, natural sounding synthetic conversation than attempting to directly extract conversation sounds from the brain,” explains professor Kamuro.

2.Clearly spoken

 To assess the intelligibility of the synthesized conversation, the researchers conducted listening tasks based on single-word identification and sentence-level transcription. In the first task, which evaluated 957,382 words, they found that listeners were better at identifying words as syllable length increased and the number of word choices (255 or 1024) decreased, consistent with natural conversation perception.

 For the sentence-level tests, the listeners heard synthesized sentences and transcribed what they heard by selecting words from a defined pool (of either 255 or 1024 words) including target and random words. In trials of 101 sentences, at least one listener was able to provide a perfect transcription for 92.7 sentences with a 255-word pool and 60 sentences with a 1024-word pool.  The transcribed sentences had a median word error rate of 96.1% with a 255-word pool size and 98.4% with a 1024-word pool.

“This level of intelligibility for neurally synthesized conversation would already be immediately meaningful and practical for real world application,” professor Kamuro write.

3.Restoring communication

 While the above tests were conducted in subjects with normal conversation, the team’s main goal is to create a device for people with communication disabilities. To simulate a setting where the subject cannot vocalize, the researchers tested their translatorULSI on silently mimed conversation.

 For this, participants were asked to speak sentences and then mime them, making the same articulatory movements but without sound. “Afterwards, we ran our conversation translatorULSI to translate these neural recordings, and we were able to generate conversation,” explains Professor Kamuro. “It was really remarkable that we could still generate audio signals from an act that did not create audio at all.”

 So how can person who cannot speak be trained to use the device? “If someone can’t speak, then we don’t have a conversation synthesizer for that person,” explains professor Kamuro.  “We have used a conversation synthesizer trained on one subject and driven that by the neural activity of another subject. We have shown that this may be possible.”

 “The second stage could be trained on a healthy speaker, but the question remains: how do we train translatorULSI 1?” adds professor Kamuro. “We’re envisioning that someone could learn by attempting to move their mouth to speak — although they cannot — and then via a feedback approach learn to speak using our device.

 AERI scientists team now has two aims. “First, we want to make the technology better, make it more natural, more intelligible,” insists professor Kamuro. “There’s a lot of engineering going on in our group to figure out how to improve it.” The other challenge is to determine whether the same algorithms used for people with normal conversation will work in a population that cannot speak — a question that may require a clinical trial to answer.

*****************************************************************************

Quantum Brain Chipset & Bio Processor (BioVLSI)

 

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute

IEEE-USA Fellow

American Physical Society Fellow

PhD. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

--------------------------------------------

Bio-computer implemented with.png
Quantum Brain Chipset that recognizes brain activity and translates it into conversation
A generative deep learning AI chipset provide insight into the mental activity of the human brain

 A generative deep learning AI chipset embedded in AERI's developing state of art bio-computer can provide insight into the mental activity of the human brain

AERI interviewed professor KAMURO 
specialized in quantum theoretical physics to Weigh in.

Quantum Brain Chipset Review 
to Quantum Brain & Biocomputer

(Quantum Brain Science and Technol
ogy)

a generative deep learning AI chipset.jpg

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼

 A generative deep learning AI chipset embedded in AERI's developing state of art bio-computer can provide insight into the mental activity of the human brain

 A research collaboration headed up at the Artificial Evolution Research Institute (AERI) has successfully employed a generative deep learning AI chipset embedded in AERI's developing state of art bio-computer to investigate the cellular architecture of the human brain.

 The approach uses data generated by AERI’s generative deep learning AI chipset embedded in AERI's developing state of art bio-computer implemented with brain computer interface (BCI) to automatically estimate brain parameters, enabling neuroscientists to infer the cellular properties of different brain regions without having to surgically probe the brain.

 The AERI scientists say that their state of art technique could potentially be used to assess treatment of neurological disorders or develop new therapies .

 

 “The underlying pathways of many diseases occur at the cellular level, and many pharmaceuticals operate at the micro scale level,” explains team leader professor Kamuro. “To know what really happens at the innermost levels of the human brain, it is crucial for us to develop methods that can delve into the depths of the brain non-invasive.”

 

 Currently, most human brain studies employ non-invasive approaches such as AERI bio-computer which limits examination of the brain at a cellular level. To bridge this gap between non-invasive imaging and cellular insight, scientists around the world have used biophysical brain models to simulate brain activity.

However, many of these models rely on overly simplistic assumptions, such as assuming that all brain regions have the same cellular properties, which is known to be incorrect.

 

 Professor Kamuro and his scientists team, working with researchers from a famous university in the top 5 in the world, analyzed imaging data from 6,785,249 participants of the Human Connectome Project. In contrast to previous modeling work, they allowed each brain region to have distinct cellular properties and exploited machine learning algorithms to automatically estimate the model parameters.

 

 “Our approach achieves a much better fit with real data,” says professor Kamuro. “Furthermore, our scientists team discovered that the micro-scale model parameters estimated by the machine learning algorithm reflect how the brain processes information.”

 

 A generative deep learning AI chipset embedded in AERI's developing state of art bio-computer can provide insight into the mental activity of the human brain.

The AERI scientists found that brain regions involved in sensory perception, such as vision, hearing and touch, exhibited cellular properties opposite to those from brain regions involved in internal thought and memories. The spatial pattern of the human brain’s cellular architecture closely reflects how the brain hierarchically processes information from the surroundings. This form of hierarchical processing is a key feature of both the human brain and recent advances in artificial intelligence.

 

 “Our study suggests that the processing hierarchy of the brain is supported by micro-scale differentiation among its regions, which may provide further clues for breakthroughs in AERI  generative deep learning AI chipset embedded in AERI's developing state of art bio-computer.” explains professor Kamuro.

 

 Moving forward, the AERI scientists plan to apply their approach to examine the brain data of individual participants, to better understand how individual variation in the brain’s cellular architecture may relate to differences in cognitive abilities. They hope that these latest results will provide a step towards the development of individualized treatment plans with specific drugs or brain stimulation strategies.

*****************************************************************************

Quantum Brain Chipset & Bio Processor (BioVLSI)

 

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute

IEEE-USA Fellow

American Physical Society Fellow

PhD. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

--------------------------------------------

 Brain implanted AERI's state of art bio-computer allows person with complete paralysis to communicate

AERI asked professor KAMURO 
specialized in quantum theoretical physics to Weigh in.

Quantum Brain Chipset Review 
to Quantum Brain & Biocomputer

(Quantum Brain Science and Technol
ogy)

could quantum physics finally explain consciousness.png

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼

1.Brain implants

​ Brain implants, often referred to as neural implants, are technological devices that connect directly to a biological subject's brain – usually placed on the surface of the brain, or attached to the brain's cortex. A common purpose of modern brain implants and the focus of much current research is establishing a biomedical prosthesis circumventing areas in the brain that have become dysfunctional after a stroke or other head injuries. This includes sensory substitution, e.g., in vision. Other brain implants are used in animal experiments simply to record brain activity for scientific reasons. Some brain implants involve creating interfaces between neural systems and computer chips. This work is part of a wider research field called brain–computer interfaces.  Brain–computer interface research also includes technology such as EEG arrays that allow interface between mind and machine but do not require direct implantation of a device.

 Brain implants electrically stimulate, block or record or both record and stimulate simultaneously signals from single neurons or groups of neurons (biological neural networks) in the brain. The blocking technique is called intra-abdominal vagal blocking. This can only be done where the functional associations of these neurons are approximately known. Because of the complexity of neural processing and the lack of access to action potential related signals using neuroimaging techniques, the application of brain implants has been seriously limited until recent advances in neurophysiology and computer processing power. Much research is also being done on the surface chemistry of neural implants in effort to design products which minimize all negative effects that an active implant can have on the brain, and that the body can have on the function of the implant. Researchers are also exploring a range of delivery systems, such as using veins, to deliver these implants without brain surgery; by leaving the skull sealed shut, patients could receive their neural implants without running as great a risk of seizures, strokes, or permanent neural impairments, all of which can be caused by open-brain surgery.

 

2.Computer implants in the brain : the state-of-the-art brain implant-type bio-computer

 We visited the AERI research laboratory in May 2023, and had interviewed Professor Kamuro, who specializes in theoretical quantum physics and brain science, about the state-of-the-art brain implant-type bio-computer that AERI scientists team is researching to Weigh in.

 “Brain implanted AERI's state of art bio-computer allows person with complete paralysis to communicate.”, professor Kamuro insisted, “A brain implanted with a state-of-the-art bio-computer embedded with a generative deep learning AI-powered chipset and brain machine interface (BCI), which is under development at AERI, will enable even paralyzed people to communicate.”

 Neural implants such as deep brain stimulation and Vagus nerve stimulation are increasingly becoming routine for patients with Parkinson's disease and clinical depression,[citation needed] respectively.

 A generative deep learning AI chipset embedded in AERI's developing state of art bio-computer can provide insight into the mental activity of the human brain.

 A research collaboration headed up at the Artificial Evolution Research Institute (AERI) has successfully employed a generative deep learning AI chipset embedded in AERI's developing state of art bio-computer to investigate the cellular architecture of the human brain.

 The approach uses data generated by AERI’s generative deep learning AI chipset embedded in AERI's developing state of art bio-computer implemented with brain computer interface (BCI) to automatically estimate brain parameters, enabling neuroscientists to infer the cellular properties of different brain regions without having to surgically probe the brain.

 The AERI scientists say that their state of art technique could potentially be used to assess treatment of neurological disorders or develop new therapies.

“The underlying pathways of many diseases occur at the cellular level, and many pharmaceuticals operate at the micro scale level,” explains team leader professor Kamuro. “To know what really happens at the innermost levels of the human brain, it is crucial for us to develop methods that can delve into the depths of the brain non-invasive.”

 Currently, most human brain studies employ non-invasive approaches such as AERI bio-computer which limits examination of the brain at a cellular level. To bridge this gap between non-invasive imaging and cellular insight, scientists around the world have used biophysical brain models to simulate brain activity.

However, many of these models rely on overly simplistic assumptions, such as assuming that all brain regions have the same cellular properties, which is known to be incorrect.

brain implemented.png

Generating language: Neural data recorded by an implanted brain–computer interface((BCI) are decoded and analyzed in real time to control spelling software.

3.AERI Bio-Computer Architecture and Principle of Operation

​ The bio-computer under development at AERI implemented with a state-of-the-art quantum brain chipset. The above brain-computer interface (BCI or BMI) consists of a bidirectional quantum bio-interference device (neural/bio connection device) implemented with direct biological connections to 130 billion human brain cells and cranial nerves. Quantum brain chipset recognizes brain activity and translates it into conversation.

 Bidirectional quantum interference devices (neural connection devices : BCI or BMI)) under study at AERI are formed with state-of-the-art CMOS organic semiconductors technology with a gate length of 1 μm rule on a flexible substrate. Bidirectional quantum interference devices (neural connection devices) under study at AERI are formed by integrating about 200 billion elements of  state-of-the-art CMOS organic semiconductors with memory functions with a gate length of 1 μm rule on a flexible substrate with excellent bio-compatibility.

 Our state-of-the-art quantum brain chipset consists of a large number of circuit block ULSI groups that make up the system, such as state-of-the-art arithmetic processors and large memory devices. The circuit block ULSI group is implemented with state-of-the-art arithmetic processing ULSIs with an integration of 3 billion state-of-the-art CMOS transistors and a memory LSIs with an integration of 8 billion transistors.

 Nurological conditions or injuries that result in the inability to communicate can be devastating. Patients with such conversation loss often rely on alternative communication devices that use brain–computer/machine interfaces (BCIs or BMIs) or nonverbal head or eye movements to control a cursor to spell out words. While these systems can enhance quality-of-life, they can only produce around 5–10 words per minute, far slower than the natural rate of human conversation.

 AERI Researchers today published details of a neural translatorULSI that can transform brain activity into intelligible synthesized conversation at the rate of a fluent speaker.

 “It has been a longstanding goal of our lab to create technology to restore communication for patients with severe conversation disabilities,” explains Quantum Physicist、Brain Scientist professor Kamuro. “We want to create technologies that can generate synthesized conversation directly from human brain activity. This study provides a proof-of-principle that this is possible.”

 Professor Kamuro and colleagues developed a method to synthesize conversation using brain signals related to the movements of a patient’s jaw, larynx, lips and tongue. To achieve this, they recorded high-density electrocorticography signals from five participants undergoing intracranial monitoring for epilepsy treatment. They tracked the activity of areas of the brain that control conversation and articulator movement as the volunteers spoke several hundred sentences.

 To reconstruct conversation, rather than transforming brain signals directly into audio signals, the researchers used a two-stage approach. First, they designed a recurrent neural network that translated the neural signals into movements of the vocal tract. Next, these movements were used to synthesize conversation.

 

4.Wireless Device

 According to professor Kamuro, the study answers a long-standing question about whether people with complete locked-in syndrome (CLIS) – who have lost all voluntary muscle control, including movement of the eyes or mouth – also lose the ability of their brain to generate commands for communication. Successful communication has previously been demonstrated with brain computer interfaces (BCIs) in individuals with paralysis but, to professor Kamuro’s knowledge, this study is the first to achieve communication by someone who has no remaining voluntary movement and hence for whom the BCI is now the sole means of communication.

 “For our participant there are no alternative approaches for communication. Professor Kamuro has amyotrophic lateral sclerosis (ALS) – a progressive neurodegenerative disease in which people lose all ability to talk and to move. He cannot move his eyes nor voluntarily move any muscles, so the BCI is his only means of communicating with his family and medical caregivers,” notes professor Kamuro.

 At present, the BCI system used in the study is for clinical investigations only and is not available outside a research setting. Further demonstrations of its longevity, applicability in other patients, safety and efficacy are needed before it is suitable for widespread clinical use.

 “At AERI science research laboratory, professor Kamuro and his developing team are currently developing ABILITY, a wireless implantable BCI device for clinical use in people with CLIS,” Zimmerman adds. “Use of a wireless device avoids the potential infection risk associated with the percutaneous cable that connects the electrodes to the computer. Professor Kamuro and his colleague also plan to use this system for speech decoding. The next step is validating the new ABILITY device in pre-clinical and clinical trials.”

*****************************************************************************

Quantum Brain Chipset & Bio Processor (BioVLSI)

 

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute( AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

American Physical Society Fellow

PhD. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

Brain implanted AERI's state of art bio-computer allows person with complete paralysis to communicate

 Re-programmable self-assembly of cranial nerves and brain cells embeds AERI's cutting-edge molecular bio-computer in the brain

AERI asked professor KAMURO 
specialized in quantum theoretical physics to Weigh in.

Quantum Brain Chipset Review 
to Quantum Brain & Biocomputer

(Quantum Brain Science and Technol
ogy)

Re-programmable self-assembly of cranial nerves and brain cells.png

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics, California Institute of Technology

IEEE-USA Fellow

Ph.D. & Dr. Kazuto Kamuro

AERI:Artificial EvolutionResearch Institute

Pasadena, California

HP: https://www.aeri-japan.com/

✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼

1. Molecular Bio-computer
 Reprogrammable self-assembly of DNA makes brain implanted AERI's state of art molecular bio-computer. AERI’s scientists have designed a tile set of DNA molecules that can carry out robust reprogrammable computations to execute six-bit algorithms and perform a variety of simple tasks. The system, which works thanks to the self-assembly of DNA strands designed to fit together in different ways while executing the algorithm, is an important milestone in constructing a universal DNA-based computing device.
 The molecular bio-computer system makes use of DNA’s ability to be programmed through the arrangement of its molecules. 
 Each strand of DNA consists of a backbone and four types of molecules known as nucleotide bases – adenine, thymine, cytosine, and guanine (A, T, C, and G) – that can be arranged in any order. This order represents information that can be used by biological cells or, as in this case, by artificially engineered DNA molecules. The A, T, C, and G have a natural tendency to pair up with their counterparts: A base pairs with T, and C pairs with G. And a sequence of bases pairs up with a complementary sequence: ATTAGCA pairs up with TGCTAAT (in the reverse orientation), for example.

Re-programmable self-assembly of cranial nerves and brain cells 2.png
Re-programmable self-assembly of cranial nerves and brain cells3.png
Re-programmable self-assembly of cranial nerves and brain cells4.png
Re-programmable self-assembly of cranial nerves and brain cells 5.png

2.The DNA tile

 An essential building block in DNA nanotechnology for programmable self-assembly of molecular bio-computer is the DNA tile, which binds through the above Watson-Crick complementary domains to several neighbors arranged on a 1, 2 or 3D lattice. Only a few DNA tile types (each with the same motif but making use of different sequences) are needed to program the self-assembly of large, micron-sized, crystalline structures in which each tile appears periodically.

 In molecular bio-computer , the DNA tile set is a collection of short (42 nucleotide) DNA strands designed to fit together into a fabric like a jigsaw puzzle. “Unlike a jigsaw puzzle, however, where each piece fits exactly in one place, molecular bio-computer algorithmic tiles can fit together in many different ways depending on the ‘input’,” explains professor Kamuro from the California Institute of Technology (Caltech), who co-led this research study. “The input here is another self-assembled DNA structure, called ‘DNA origami’, which can be designed to carry DNA strands representing any six-bit binary number of choice.”

3.An arbitrary six-bit Boolean circuit

 In the molecular bio-computer, the input structure in fact acts as a seed for the crystallization of the DNA tiles, forming a DNA nanotube that carries out a computation based on how the tiles fit together, professor Kamuro adds. “Roughly speaking, each new layer of growth on the nanotube represents one more iteration of a six-bit Boolean circuit. Our scientists team can program the circuit by choosing which strands from a master set are mixed together in a given experiment.

 The researchers say in the molecular bio-computer  they can program a master set of 4755 DNA tiles (byselecting the right subset of 100 strands) to compute an arbitrary six-bit circuit. “There are 244 (17 trillion) such circuits possible and although not all of these are interesting, the scientific team plemented 2,775 circuits that display a surprisingly wide range of behaviors,” explains professor Kamuro.

 “My favorite is an odd little circuit that acts like a clock. It starts with 000010, goes to 011001 and then to 001010. Next, and in a strange order, it goes to 60 other bit patterns before returning to 000010 and repeating the sequence.”

 The experiment is limited to six-bit inputs because of certain technical engineering constraints, related to the geometry of the DNA nanotubes, explains study co-lead author from the California Institute of Technology (Caltech)University of California, Kamuro. “In the molecular bio-computer, with the right DNA tile design though, there’s every reason to think that we could make systems processing far more bits as reliably, or even more so, by amplifying our error-correction techniques in ways we already know.”

 As well as the clock, the other circuits the team ran include: a circuit that determines whether a six-bit binary number is a multiple of three; one that determines whether it is a palindrome (that is, reads the same forwards and backwards); one that tests whether the numbers of 1s is even or odd; one that tests for equality with 21 (that is, 010101 in binary); and a circuit that sorts all the 1s to the left and the 0s to the right.

4.Randomized circuits

 The researchers also made some randomized circuits. These occur when molecules are included for more than one function that could be computed at a specific location, explains Kamuro. “For example, in the molecular bio-computer, we considered a molecule that binds to input (0,1) at bit positions 3 and 4, outputting (1,1). If we also include a molecule that binds to (0,1) at bit positions 3 and 4, but presents outputs that encode (0,0), then the computation that occurs depends on which of these two molecules arrives first which is a random process.

“We can then design such circuits that grow random patterns – such as a single bit that performs a random walk up and down the array of bits. Such circuits are important in theoretical molecular bio-computer science, he says.

 

5.Programming at the bench

 All the DNA circuits in the molecular bio-computer are made directly in a test tube, which essentially contain billions of completed algorithms, each resembling a knitted scarf of DNA tiles. The pattern on the scarf provides the solution to the algorithm that was run.

“In previous work researchers might have designed a new system and then waited days or even weeks for the DNA stands to be created by a DNA synthesis company and shipped to their lab,” explains study co-lead author professor Kamuro. Kamuro performed this study while at Caltech and then at INRIA in France. “We wanted to w\-[-rite programs and then run them immediately. To do this, we designed a set of 2,355 DNA strands that could implement any program in our six-bit circuit model and then stored these formulations in the fridge.

 ”Running the circuits in the molecular bio-computer is then fairly simple, Kamuro tells. “We write the circuit design in a text file and the molecular bio-computer program converts the circuit description into list of a 100 or so names of our stored DNA strands. We then select these strands and mix them in a test tube with other strands that encode the six-bit input to the circuit. We then read out these circuits by imaging the patterns on them using an atomic force microscope.”

 The AERI’s scientists say that if they could reliably scale up their six-bit DNA circuit to much larger numbers, it might be used to make a universal set of DNA tiles that could be reprogrammed to grow any type of structure describable by an algorithm in the molecular bio-computer.

 “In this scenario, you could, for instance, write a standard computer program that draws a smiley face or flower to the screen,” says Kamuro . “That program is simply a sequence of bits. If you encode those bits into a seed structure (as our experiment’s seed encoded six bits), the tiles would execute the program and use its output to know exactly where they need to grow to make a smiley face or a flower.”

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Quantum Brain Chipset & Bio Processor (BioVLSI)

 

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute( AERI: https://www.aeri-japan.com/

IEEE-USA Fellow

American Physical Society Fellow

PhD. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

Re-programmable self-assembly of cranial nerves and brain cells embeds AERI's cutting-edge molecular bio-computer in the brain

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